Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for impro...Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.展开更多
The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring ...The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.展开更多
Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while ...Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.展开更多
We describe clinical, diagnostic features and follow up of a patient with a vanishing brain lesion. A 14-yearold child admitted to the department of Neurology at September 2009 with a history of subacute onset of feve...We describe clinical, diagnostic features and follow up of a patient with a vanishing brain lesion. A 14-yearold child admitted to the department of Neurology at September 2009 with a history of subacute onset of fever, anorexia, vomiting, blurring of vision and right hemiparesis since one month. Magnetic resonance imaging(MRI) of the brain revealed presence of intraaxial large mass(25 mm × 19 mm) in the left temporal lobe and the brainstem which showed hypointense signal in T1 W and hyperintense signals in T2 W and fluid attenuated inversion recovery(FLAIR) images and homogenously enhanced with gadolinium(Gd). It was surrounded by vasogenic edema with mass effect. Intravenous antibiotics, mannitol(2 g/12 h per 2 d) and dexamethasone(8 mg/12 h) were given to relief manifestations of increased intracranial pressure. Whole craniospinal radiotherapy(brain = 4000 CGy/20 settings per 4 wk; Spinal = 2600/13 settings per 3 wk) was given based on the high suspicion of neoplastic lesion(lymphoma or glioma). Marked clinical improvement(up to complete recovery) occurred within 15 d. Tapering of the steroid dose was done over the next 4 mo. Follow up with MRI after 3 mo showed small lesion in the left antero-medial temporal region with hypointense signal in T1 W and hyperintense signals in T2 W and FLAIR images but did not enhance with Gd. At August 2012, the patient developed recurrent generalized epilepsy. His electroencephalography showed the presence of left temporal focus of epileptic activity. MRI showed the same lesion as described in the follow up. The diffusion weighted images were normal. The seizures frequency was decreased with carbamazepine therapy(300 mg/12 h). At October 2014, single voxel proton(1H) MR spectroscopy(MRS) showedreduced N-acetyl-aspartate(NAA)/creatine(Cr), choline(Cho)/Cr, NAA/Cho ratios consistent with absence of a neoplasm and highly suggested presence of gliosis. A solitary brain mass in a child poses a considerable diagnostic difficulty. MRS provided valuable diagnostic differentiation between tumor and pseudotumor lesions.展开更多
BACKGROUND This case series investigated the clinical manifestations,diagnoses,and treatment of cerebral abscesses caused by Streptococcus anginosus.We retrospectively analyzed the clinical characteristics and outcome...BACKGROUND This case series investigated the clinical manifestations,diagnoses,and treatment of cerebral abscesses caused by Streptococcus anginosus.We retrospectively analyzed the clinical characteristics and outcomes of three cases of cerebral abscesses caused by Streptococcus anginosus and conducted a comprehensive review of relevant literature.CASE SUMMARY Case 1 presented with a history of left otitis media and exhibited high fever,confusion,and vomiting as primary symptoms.Postoperative pus culture indicated a brain abscess caused by Streptococcus constellatus infection.Case 2 experienced dizziness for two days as the primary symptom.Postoperative pus culture suggested an intermediate streptococcal brain abscess.Case 3:Enhanced head magnetic resonance imaging(MRI)and diffusion-weighted imaging revealed occupancy of the left temporal lobe,initially suspected to be a metastatic tumor.However,a postoperative pus culture confirmed the presence of a brain abscess caused by Streptococcus anginosus infection.The three cases presented in this case series were all patients with community-acquired brain abscesses resulting from angina caused by Streptococcus group infection.All three patients demonstrated sensitivity to penicillin,ceftriaxone,vancomycin,linezolid,chloramphenicol,and levofloxacin.Successful treatment was achieved through stereotaxic puncture,drainage,and ceftriaxone administration with a six-week course of antibiotics.CONCLUSION Preoperative enhanced head MRI plays a critical role in distinguishing brain tumors from abscesses.Selecting the correct early diagnostic methods for brain abscesses and providing timely intervention are very important.This case series was in accordance with the CARE guidelines.展开更多
Research scientists and clinicians should be aware that missed diagnoses of mild-moderate traumatic brain injuries in post-acute patients having spinal cord injuries may approach 60-74% with certain risk factors, pote...Research scientists and clinicians should be aware that missed diagnoses of mild-moderate traumatic brain injuries in post-acute patients having spinal cord injuries may approach 60-74% with certain risk factors, potentially causing clinical consequences for patients, and confounding the results of clinical research studies. Factors leading to a missed diagnosis may include acute trauma-related life-threatening issues, sedation/intubation, subtle neuropathology on neuroimaging, failure to collect Glasgow Coma Scale scores or duration of posttraumatic amnesia, or lack of validity of this information, and overlap in neuro-cognitive symptoms with emotional responses to spinal cord injuries. Strategies for avoiding a missed diagnosis of mild-moderate traumatic brain injuries in patients having a spinal cord injuries are highlighted in this perspective.展开更多
Objective To investigate th e value of proton magnetic resonance spectroscopy ( 1H-MRS) on diagnosis a nd differential diagnosis of the intracranial diseases by the MRS results of 52 patients. Methods 12 patients ...Objective To investigate th e value of proton magnetic resonance spectroscopy ( 1H-MRS) on diagnosis a nd differential diagnosis of the intracranial diseases by the MRS results of 52 patients. Methods 12 patients with benign glioma, 16 patients with malignant glioma, 10 patients with meningioma, 8 patients with virus encephalitis, and 6 patients with cerebral infarction underwent MRS in th e lesion region. We measured the area within the spectra of N-acetyl-aspartate (NAA), creatine/phosphocreatine (Cr), choline compounds (Cho), and lactate (Lac ). Results The spectra of meningiomas were characterized by abs ence of NAA. The spectra of gliomas were characterized by the decrease of NAA an d Cr, but the increase of Cho. The ratio of Cho to Cr was 2.25±1.21 in benign g liomas, while the ratio of Cho to Cr was 4.65±2.21 in malignant gliomas. The sp ectra of virus encephalitis appeared the decrease of NAA and the normality of Cr , with the 1.25±0.21 ratio of Cho/Cr. The apparent Lac wave could be seen in al l cerebral infarctions. Conclusion The value of 1H-MRS plays a significant role in the diagnosis and differential diagnosis of gliomas, mening iomas, virus encephalitis, and cerebral infarctions.展开更多
BACKGROUND Cerebral mucormycosis is an infectious disease of the brain caused by fungi of the order Mucorales.These infections are rarely encountered in clinical practice and are often misdiagnosed as cerebral infarct...BACKGROUND Cerebral mucormycosis is an infectious disease of the brain caused by fungi of the order Mucorales.These infections are rarely encountered in clinical practice and are often misdiagnosed as cerebral infarction or brain abscess.Increased mortality due to cerebral mucormycosis is closely related to delayed diagnosis and treatment,both of which present unique challenges for clinicians.CASE SUMMARY Cerebral mucormycosis is generally secondary to sinus disease or other disseminated disease.However,in this retrospective study,we report and analyze a case of isolated cerebral mucormycosis.CONCLUSION The constellation of symptoms including headaches,fever,hemiplegia,and changes in mental status taken together with clinical findings of cerebral infarction and brain abscess should raise the possibility of a brain fungal infection.Early diagnosis and prompt initiation of antifungal therapy along with surgery can improve patient survival.展开更多
The authors present 83 patients with mixed glioma with experiences in clinical diagnosis and treatment.In all these cases.there were 44 tumors as grade 1 or 2,and 39 as grade 3 or 4.In 39 tumors.two glial components(o...The authors present 83 patients with mixed glioma with experiences in clinical diagnosis and treatment.In all these cases.there were 44 tumors as grade 1 or 2,and 39 as grade 3 or 4.In 39 tumors.two glial components(oligodendrocytes and astrocytes) occurr展开更多
This article is presenting data from a retrospective analysis of medical records and computed tomography (CT) scans of patients’ chests with coronavirus infection “COVID-19” who applied to the diagnostic center of ...This article is presenting data from a retrospective analysis of medical records and computed tomography (CT) scans of patients’ chests with coronavirus infection “COVID-19” who applied to the diagnostic center of URFA in Osh during the first wave of the pandemic in the Kyrgyz Republic, with a description of individual clinical cases and their differential diagnosis. Chest computed tomography is one of the main methods in visual diagnosis of pneumonia in COVID-19 in hospitalized patients, which allows determining signs, symptoms for effective treatment.展开更多
According to the World Health Organization(WHO),Brain Tumors(BrT)have a high rate of mortality across the world.The mortality rate,however,decreases with early diagnosis.Brain images,Computed Tomography(CT)scans,Magne...According to the World Health Organization(WHO),Brain Tumors(BrT)have a high rate of mortality across the world.The mortality rate,however,decreases with early diagnosis.Brain images,Computed Tomography(CT)scans,Magnetic Resonance Imaging scans(MRIs),segmentation,analysis,and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages.For physicians,diagnosis can be challenging and time-consuming,especially for those with little expertise.As technology advances,Artificial Intelligence(AI)has been used in various domains as a diagnostic tool and offers promising outcomes.Deep-learning techniques are especially useful and have achieved exquisite results.This study proposes a new Computer-Aided Diagnosis(CAD)system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered tumors.The segmentation mechanism is used to determine the shape,area,diameter,and outline of any tumors,while the classification mechanism categorizes the type of cancer as slow-growing or aggressive.The main goal is to diagnose tumors early and to support the work of physicians.The proposed system integrates a Convolutional Neural Network(CNN),VGG-19,and Long Short-Term Memory Networks(LSTMs).A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of tumors.Numerous experiments have been conducted on different five datasets to evaluate the presented system.These experiments reveal that the system achieves 97.98%average accuracy when the segmentation and classification functions were utilized,demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI images.In addition,the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’lives and avoid the high cost of treatments.展开更多
The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applica...The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.展开更多
Objective:To explore level diagnosis on CT and BA in cerebrovascular diseases.Method:CT and BA were examined in 53 patients with cerebrovascular diseases and compared in level diagnosis.Result:The sides on level diago...Objective:To explore level diagnosis on CT and BA in cerebrovascular diseases.Method:CT and BA were examined in 53 patients with cerebrovascular diseases and compared in level diagnosis.Result:The sides on level diagonsis of CT and BA were identical.The rang of diseases was larger in BA than that in CT.Conclusion:BA can help level diagnosis in cerebrovascular diseases.The level diagnosis of BA and CT were basically identical.展开更多
基金supported by the 2022 Yeungnam University Research Grant.
文摘Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate.The location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and time.An automatic recognition,place,and classifier process was desired and useful.This study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor Diagnosis.The presentedADRUSCM model majorly focuses on the segmentation and classification of BT.To accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in it.In addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions.Moreover,VGG-19 model is exploited as a feature extractor.Finally,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes theGRUhyperparameters.The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.
文摘The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Aziz University under the Research Project (PSAU/2023/01/22425).
文摘Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.
文摘We describe clinical, diagnostic features and follow up of a patient with a vanishing brain lesion. A 14-yearold child admitted to the department of Neurology at September 2009 with a history of subacute onset of fever, anorexia, vomiting, blurring of vision and right hemiparesis since one month. Magnetic resonance imaging(MRI) of the brain revealed presence of intraaxial large mass(25 mm × 19 mm) in the left temporal lobe and the brainstem which showed hypointense signal in T1 W and hyperintense signals in T2 W and fluid attenuated inversion recovery(FLAIR) images and homogenously enhanced with gadolinium(Gd). It was surrounded by vasogenic edema with mass effect. Intravenous antibiotics, mannitol(2 g/12 h per 2 d) and dexamethasone(8 mg/12 h) were given to relief manifestations of increased intracranial pressure. Whole craniospinal radiotherapy(brain = 4000 CGy/20 settings per 4 wk; Spinal = 2600/13 settings per 3 wk) was given based on the high suspicion of neoplastic lesion(lymphoma or glioma). Marked clinical improvement(up to complete recovery) occurred within 15 d. Tapering of the steroid dose was done over the next 4 mo. Follow up with MRI after 3 mo showed small lesion in the left antero-medial temporal region with hypointense signal in T1 W and hyperintense signals in T2 W and FLAIR images but did not enhance with Gd. At August 2012, the patient developed recurrent generalized epilepsy. His electroencephalography showed the presence of left temporal focus of epileptic activity. MRI showed the same lesion as described in the follow up. The diffusion weighted images were normal. The seizures frequency was decreased with carbamazepine therapy(300 mg/12 h). At October 2014, single voxel proton(1H) MR spectroscopy(MRS) showedreduced N-acetyl-aspartate(NAA)/creatine(Cr), choline(Cho)/Cr, NAA/Cho ratios consistent with absence of a neoplasm and highly suggested presence of gliosis. A solitary brain mass in a child poses a considerable diagnostic difficulty. MRS provided valuable diagnostic differentiation between tumor and pseudotumor lesions.
基金Supported by 2024 Zhejiang Province Traditional Chinese Medicine Science and Technology Plan,No.2024ZL1129,No.2024ZL1130.
文摘BACKGROUND This case series investigated the clinical manifestations,diagnoses,and treatment of cerebral abscesses caused by Streptococcus anginosus.We retrospectively analyzed the clinical characteristics and outcomes of three cases of cerebral abscesses caused by Streptococcus anginosus and conducted a comprehensive review of relevant literature.CASE SUMMARY Case 1 presented with a history of left otitis media and exhibited high fever,confusion,and vomiting as primary symptoms.Postoperative pus culture indicated a brain abscess caused by Streptococcus constellatus infection.Case 2 experienced dizziness for two days as the primary symptom.Postoperative pus culture suggested an intermediate streptococcal brain abscess.Case 3:Enhanced head magnetic resonance imaging(MRI)and diffusion-weighted imaging revealed occupancy of the left temporal lobe,initially suspected to be a metastatic tumor.However,a postoperative pus culture confirmed the presence of a brain abscess caused by Streptococcus anginosus infection.The three cases presented in this case series were all patients with community-acquired brain abscesses resulting from angina caused by Streptococcus group infection.All three patients demonstrated sensitivity to penicillin,ceftriaxone,vancomycin,linezolid,chloramphenicol,and levofloxacin.Successful treatment was achieved through stereotaxic puncture,drainage,and ceftriaxone administration with a six-week course of antibiotics.CONCLUSION Preoperative enhanced head MRI plays a critical role in distinguishing brain tumors from abscesses.Selecting the correct early diagnostic methods for brain abscesses and providing timely intervention are very important.This case series was in accordance with the CARE guidelines.
基金Department of Physical Medicine&Rehabilitation funding by the United States Department of Education,National Institute of Disability Research and Rehabilitation#H133A120099(TBI Model Systems grant)
文摘Research scientists and clinicians should be aware that missed diagnoses of mild-moderate traumatic brain injuries in post-acute patients having spinal cord injuries may approach 60-74% with certain risk factors, potentially causing clinical consequences for patients, and confounding the results of clinical research studies. Factors leading to a missed diagnosis may include acute trauma-related life-threatening issues, sedation/intubation, subtle neuropathology on neuroimaging, failure to collect Glasgow Coma Scale scores or duration of posttraumatic amnesia, or lack of validity of this information, and overlap in neuro-cognitive symptoms with emotional responses to spinal cord injuries. Strategies for avoiding a missed diagnosis of mild-moderate traumatic brain injuries in patients having a spinal cord injuries are highlighted in this perspective.
文摘Objective To investigate th e value of proton magnetic resonance spectroscopy ( 1H-MRS) on diagnosis a nd differential diagnosis of the intracranial diseases by the MRS results of 52 patients. Methods 12 patients with benign glioma, 16 patients with malignant glioma, 10 patients with meningioma, 8 patients with virus encephalitis, and 6 patients with cerebral infarction underwent MRS in th e lesion region. We measured the area within the spectra of N-acetyl-aspartate (NAA), creatine/phosphocreatine (Cr), choline compounds (Cho), and lactate (Lac ). Results The spectra of meningiomas were characterized by abs ence of NAA. The spectra of gliomas were characterized by the decrease of NAA an d Cr, but the increase of Cho. The ratio of Cho to Cr was 2.25±1.21 in benign g liomas, while the ratio of Cho to Cr was 4.65±2.21 in malignant gliomas. The sp ectra of virus encephalitis appeared the decrease of NAA and the normality of Cr , with the 1.25±0.21 ratio of Cho/Cr. The apparent Lac wave could be seen in al l cerebral infarctions. Conclusion The value of 1H-MRS plays a significant role in the diagnosis and differential diagnosis of gliomas, mening iomas, virus encephalitis, and cerebral infarctions.
文摘BACKGROUND Cerebral mucormycosis is an infectious disease of the brain caused by fungi of the order Mucorales.These infections are rarely encountered in clinical practice and are often misdiagnosed as cerebral infarction or brain abscess.Increased mortality due to cerebral mucormycosis is closely related to delayed diagnosis and treatment,both of which present unique challenges for clinicians.CASE SUMMARY Cerebral mucormycosis is generally secondary to sinus disease or other disseminated disease.However,in this retrospective study,we report and analyze a case of isolated cerebral mucormycosis.CONCLUSION The constellation of symptoms including headaches,fever,hemiplegia,and changes in mental status taken together with clinical findings of cerebral infarction and brain abscess should raise the possibility of a brain fungal infection.Early diagnosis and prompt initiation of antifungal therapy along with surgery can improve patient survival.
文摘The authors present 83 patients with mixed glioma with experiences in clinical diagnosis and treatment.In all these cases.there were 44 tumors as grade 1 or 2,and 39 as grade 3 or 4.In 39 tumors.two glial components(oligodendrocytes and astrocytes) occurr
文摘This article is presenting data from a retrospective analysis of medical records and computed tomography (CT) scans of patients’ chests with coronavirus infection “COVID-19” who applied to the diagnostic center of URFA in Osh during the first wave of the pandemic in the Kyrgyz Republic, with a description of individual clinical cases and their differential diagnosis. Chest computed tomography is one of the main methods in visual diagnosis of pneumonia in COVID-19 in hospitalized patients, which allows determining signs, symptoms for effective treatment.
文摘According to the World Health Organization(WHO),Brain Tumors(BrT)have a high rate of mortality across the world.The mortality rate,however,decreases with early diagnosis.Brain images,Computed Tomography(CT)scans,Magnetic Resonance Imaging scans(MRIs),segmentation,analysis,and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages.For physicians,diagnosis can be challenging and time-consuming,especially for those with little expertise.As technology advances,Artificial Intelligence(AI)has been used in various domains as a diagnostic tool and offers promising outcomes.Deep-learning techniques are especially useful and have achieved exquisite results.This study proposes a new Computer-Aided Diagnosis(CAD)system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered tumors.The segmentation mechanism is used to determine the shape,area,diameter,and outline of any tumors,while the classification mechanism categorizes the type of cancer as slow-growing or aggressive.The main goal is to diagnose tumors early and to support the work of physicians.The proposed system integrates a Convolutional Neural Network(CNN),VGG-19,and Long Short-Term Memory Networks(LSTMs).A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of tumors.Numerous experiments have been conducted on different five datasets to evaluate the presented system.These experiments reveal that the system achieves 97.98%average accuracy when the segmentation and classification functions were utilized,demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI images.In addition,the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’lives and avoid the high cost of treatments.
基金funded and supported by the Taif University Researchers,Taif University,Taif,Saudi Arabia,under Project TURSP-2020/147.
文摘The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.
文摘Objective:To explore level diagnosis on CT and BA in cerebrovascular diseases.Method:CT and BA were examined in 53 patients with cerebrovascular diseases and compared in level diagnosis.Result:The sides on level diagonsis of CT and BA were identical.The rang of diseases was larger in BA than that in CT.Conclusion:BA can help level diagnosis in cerebrovascular diseases.The level diagnosis of BA and CT were basically identical.
