L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

In vivo tumor detection with com bined MR-Photoacoustic-Thermoacoustic imaging

Here,we report a new method using combined magnetic resonance(MR)-Photoacoustic(PA)-Thermoacoustic(TA)imaging techmiques,and demonstrate its unique ability for in vrivo cancer detection using tumor-bearing mice.Circular scanning TA and PA imaging systems were used to recover the dielectric and optical property dist ributions of three colon carcinoma bearing mice While a 7.0-T magnetic resonance imaging(MRI)unit with a mouse body volume coil was utilized for high resolution structural imaging of the same mice.Three plastic tubes flled with soybean sauce were used as fiducial markers for the co-registration of MR,PA and TA images.The resulting fused images provided both enhanced tumor margin and contrast relative to the surrounding normal tissues.In particular,some finger-like protrusions extending into the surrounding tissues were revealed in the MR/TA infused images.These results show that the tissue functional optical and dielectric properties provided by PA and TA images along with the anatomical structure by MRI in one picture make accurate tumor identification easier.This combined MR-PA-TA-imaging strategy has the potential to offer a dinically useful triple-modality tool for accurate cancer detection and for intraoper ative surgical navigation.

An Effective Diagnosis System for Brain Tumor Detection and Classification

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain.This growth is considered deadly since it may cause death.The brain controls numerous functions,such as memory,vision,and emotions.Due to the location,size,and shape of these tumors,their detection is a challenging and complex task.Several efforts have been conducted toward improved detection and yielded promising results and outcomes.However,the accuracy should be higher than what has been reached.This paper presents a method to detect brain tumors with high accuracy.The method works using an image segmentation technique and a classifier in MATLAB.The utilized classifier is a SupportVector Machine(SVM).DiscreteWavelet Transform(DWT)and Principal Component Analysis(PCA)are also involved.A dataset from the Kaggle website is used to test the developed approach.The obtained results reached nearly 99.2%of accuracy.The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature.This evaluation shows that the presented system outperforms other approaches regarding the accuracy,precision,and recall.This research discovered that the developed method is extremely useful in detecting brain tumors,given the high accuracy,precision,and recall results.The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.

Nonlinear Teager-Kaiser Infomax Boost Clustering Algorithm for Brain Tumor Detection Technique

Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation.When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great tool.It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture.Radiologists have a difficult time sorting and classifying tumors from multiple images.Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are processed.Different clinical characteristics are then retrieved and analyzed statistically to identify brain tumors.The use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative research.This dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture count.This segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors

As a form of artificial intelligence,artificial neural networks(ANNs)have the advantages of adaptability,parallel processing capabilities,and non-linear processing.They have been widely used in the early detection and diagnosis of tumors.In this article,we introduce the development,working principle,and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.

Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human breast.The size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast phantom.The operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of antenna.After the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor cells.The simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.

Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

Early detection of circulating tumor DNA and successful treatment with osimertinib in thr790met-positive leptomeningeal metastatic lung cancer:A case report

BACKGROUND Patients diagnosed with non-small-cell lung cancer with activated epidermal growth factor receptor mutations are more likely to develop leptomeningeal(LM)metastasis than other types of lung cancers and have a poor prognosis.Early diagnosis and effective treatment of leptomeningeal carcinoma can improve the prognosis.CASE SUMMARY A 55-year-old female with a progressive headache and vomiting for one month was admitted to Peking University First Hospital.She was diagnosed with lung adenocarcinoma with osseous metastasis 10 months prior to admittance.epidermal growth factor receptor(EGFR)mutation was detected by genomic examination,so she was first treated with gefitinib for 10 months before acquiring resistance.Cell-free cerebrospinal fluid(CSF)circulating tumor DNA detection by next-generation sequencing was conducted and indicated the EGFR-Thr790Met mutation,while biopsy and cytology from the patient’s CSF and the first enhanced cranial magnetic resonance imaging(MRI)showed no positive findings.A month later,the enhanced MRI showed linear leptomeningeal enhancement,and the cytology and biochemical examination in CSF remained negative.Therefore,osimertinib(80 mg/d)was initiated as a second-line treatment,resulting in a good response within a month.CONCLUSION This report suggests clinical benefit of osimertinib in LM patients with positive detection of the EGFR-Thr790Met mutation in CSF and proposes that the positive findings of CSF circulating tumor DNA as a liquid biopsy technology based on the detection of cancer-associated gene mutations may appear earlier than the imaging and CSF findings and may thus be helpful for therapy.Moreover,the routine screening of chest CT with the novel coronavirus may provide unexpected benefits。

Detection of chromosomal imbalance in oligodendroglial tumors by comparative genomic hybridization

Objective To investigate the relationship between genomic DNA imbalance in oligodendroglial tumors and its different classification. Methods 16 oligodendrogliomas and 17 anaplastic oligodendrogliomas were investigated by comparative genomic hybridization on Paraffin-Embedded tissue samples,and the chromosomal genomic DNA imbalances were analyzed. Results Chromosome DNA imbalance rates in oligodendrogliomas

Scanning photoacoustic imaging of submucosal gastric tumor based on a long focused transducer in phantom and in vitro experiments

Photoacoustic imaging,which can provide the maximum intensity contrast in tissue depth imaging without ionizing radiation,will be a promising imaging trend for tumor detection.In this paper,a column diffusionfiber was employed to carry a pulsed laser for irradiating stomach directly through esophagus based on the characteristics of gastric tissue structure.A long focused ultrasonic transducer was placed outside the body to detect photoacoustic signals of gastric tissue.Phantom and in vitro experiments of submucosal gastric tumors were carried out to check the sensitivity of scanning photoacoustic tomography system,including the lateral and longitudinal resolution of the system,sensitivity of different absorption coefficient in imaging,capability of transversal detection,and probability of longitudinal detection.The results demonstrate that our innovative technique can improve the parameters of imaging.The lateral resolution reaches 2.09 mm.Then a depth of 5.5mm with a longitudinal accuracy of 0.36mm below gastric mucosa of early gastric cancer(EGC)has been achieved.In addition,the optimal absorption coefficient differences among absorbers of system are 3.3-3.9 times.Results indicate that our photoacoustic imaging(PAI)system,is based on a long focusing transducer,can provide a potential application for detecting submucosal EGC without obvious symptoms.

Long Non-coding RNA HOTAIR for Tumor Diagnosis and Pathological Staging

LncRNA HOTAIR has different expression levels in different stages of tumorigenesis and development.Therefore,it has potential application in clinical diagnosing tumor stage as a tumor marker.Fifty patients with lung cancer and fifty healthy volunteers were selected as lung cancer group and control group respectively.According to T and N stage of cancer,the patients were divided into T1,T2,T3 and N0,N1,N2 groups.Fluorescence in situ hybridization(FISH)was used to detect the expression of HOTAIR in lung cancer tissues and paraneoplastic tissues.The expression levels of NSE,CEA and CYFRA21-1 in serum were detected by immunofluorescence.The results showed that the expression level of HOTAIR in paraneoplastic tissues(0.98±0.04)were significantly lower than that in lung cancer tissues(3.56±0.15).The serum levels of NSE,CEA and CYFRA21-1 of the lung cancer group were higher than those in the control group.The concentrations of HOTAIR,NSE,CEA and CYFRA21-1 increased with the progress of clinical stages,which indicated that the expression of HOTAIR was positively correlated with the expression of SE,CEA and CYFRA21-1.These results indicate that HOTAIR,NSE,CEA and CYFRA21-1 are associated with the initiation and development of tumors.Therefore,HOTAIR combined with tumor marker can improve the accuracy of detection of pathological stage of lung cancer.

Breast Cancer Detection Based on Multi-Slotted Patch Antenna at ISM Band

The present work designed and investigated a 3D basic model for breast cancer detection at the ISM band. The model consists of two multi-slotted rectangular patch antennas and a three-layer breast phantom containing two tumors. A multi-slotted antenna was designed at 2.45 GHz using CST STUDIO SUITE 2018, where the simulated results showed a return loss better than -35 dB and attended more than 77 MHz bandwidth. The diagnosis approach is based on exploiting the electrical properties (frequency dependent) of breast tissues, i.e., mass density, relative permittivity, and conductivity. Once the proposed slotted antenna radiates electromagnetic signals toward the breast model (with and without tumors), the radiation properties in terms of the scattering parameters (S11 and S21), the electrical field, the power flow, the current density, and the power loss density were altered. As a result, the values of these radiation parameters increased when tumors were implanted inside the breast model, informing the presence of cancerous tissues. Moreover, the specific absorption rate (SAR) was estimated as a function of input powers, where the proposed antenna showed a set of low SAR values compared to the IEEE standard of 1.6 W/kg, validating its potential use for diagnosing purposes. The simulated results indicated the prospective use of two slotted antennas (in the first instance) to detect multiple tumors which could be a challenging task using a single-element antenna, where the ultimate goal is to realize a compact antenna array to detect multi-tumors.

Evaluating the Efficacy of Fecal Occult Blood Test and Tumor Marker Combined Screening for Colorectal Cancer

Objective:To analyze the screening effectiveness of combining the fecal occult blood test with tumor marker detection for colorectal cancer.Methods:A total of thirty patients with colorectal cancer and thirty patients with benign colon hyperplasia who received treatment from January 2020 to January 2023 were selected.These patients were assigned to the observation group and the control group,respectively.All patients in both groups underwent both fecal occult blood tests and tumor marker detection.The levels of tumor markers between the two groups were compared,the tumor marker levels in different stages were assessed within the observation group,and the positive detection rates for single detection and combined detection were compared.Results:The levels of various tumor markers in the observation group were significantly higher than those in the control group(P<0.05).Furthermore,as the Duke stage increased within the observation group,the levels of various tumor markers also increased(P<0.05).The positive detection rate of the combined test was notably higher than that of single detection(P<0.05).Conclusion:Combining the fecal occult blood test with tumor marker detection in colorectal cancer screening can significantly improve the overall detection rate.

Tumor Cell Detection Device Based on Surface Plasmon Resonance Imaging and Image Processing

A laboratory-made tumor cell detection device was fabricated based on both surface plasmon resonance imaging（SPRi） and image processing.In this device,a gravity-induced flow injection chip（gFIC） was exploited to replace a pump.Also two charge coupled devices（CCDs） were used to detect HepG2 cells by SPRi and image processing,respectively.The results of two CCDs are associated.Protein A was used to modify the sensing surface.The inlet angle was carefully adjusted for the device to get an enhanced image.In the test,the contrast among cell solutions at different concentrations can be easily distinguished.The other CCD using image processing can tell false-positive in some degree.This detection is label-free,real time,and precise.

Imaging probes for non-invasive tumoral detection and functional monitoring of cancer multidrug resistance

Aim:Several cationic radiotracers originally developed as myocardial perfusion agents have shown potential for both early detection of cancer and non-invasive monitoring of multiple drug resistance(MDR)by single photon emission computed tomography.We have introduced two cationic complexes,^(99m)Tc-DMEOP[di-methoxy-tris-pyrazolyl-^(99m)Tc-(CO)_(3)]and ^(99m)Tc-TMEOP[tri-methoxy-tris-pyrazolyl-^(99m)Tc-(CO)_(3)],which showed excellent preclinical results as cardiac imaging probes,namely a persistent heart uptake with rapid blood and liver clearance.This study aimed at the evaluation of their usefulness for tumoral detection and functional assessment of MDR.Methods:The uptake and efflux kinetics of ^(99m)Tc-DMEOP and ^(99m)Tc-TMEOP were evaluated in human prostate,lung,and breast cancer cell lines,including drug-resistant cell lines that are known to overexpress the MDR P-glycoprotein(Pgp).The effects of MDR modulators were also studied.In vivo studies were performed in xenografted tumor models,and the MDR phenotype of the tumors was confirmed by Western blot.Results:The uptake kinetics of both complexes in human cancer cell lines is comparable with the one found for ^(99m)Tc-Sestamibi,increasing over time.The uptake of ^(99m)Tc-TMEOP is greatly reduced in cells overexpressing Pgp and increased in the presence of a Pgp modulator.In nude mice,the tumor uptake of ^(99m)Tc-TMEOP was higher in the MCF-7 xenografts compared with the MCF7 Pgp tumors.Conclusion:The uptake kinetics of both complexes in human cancer cell lines is comparable with the one found for ^(99m)Tc-Sestamibi,increasing over time.The uptake of ^(99m)Tc-TMEOP is greatly reduced in cells overexpressing Pgp,and increased in the presence of a Pgp modulator.In nude mice,the tumor uptake of ^(99m)Tc-TMEOP was higher in the MCF-7 xenografts compared with the MCF7 Pgp tumors.

Utilizing a microfluidic device to enrich and fluorescently detect circulating tumor cells

Circulating tumor cells(CTCs)are cancer cells that have propagated from primary tumor sites,spreading into the bloodstream as the cellular origin of fatal metastasis,and to secondary tumor sites.Capturing and analyzing CTCs is a kind of‘‘liquid biopsy'of the tumor that provides information about cancer changes over time and tailoring treatment[1].CTC enrichment and detection remains technologically challenging due to their extremely low concentra-

Recent advances in molecular and nanoparticle probes for fluorescent bioanalysis

With the increasing emphasis on ecological safety and physical health,the detection and treatment of harmful substances and diseases are becoming more and more prevalent.Therefore,efficiently monitoring these biological behaviors with high accuracy and sensitivity in real-time has shown prominent research significance.The use of fluorescent probes to analyze organisms has gained momentum in recent years,especially in the field of organ imaging and assisted cancer therapy,where fluorescent bioanalysis demonstrates significant advantageous.In this review,we explored the latest advancements in fluorescent molecular probes(e.g.,small-molecule,macro-molecule,supramolecule)and fluorescent nanoparticle probes(e.g.,quantum dots or nanoclusters,metal-organic frameworks,polymers,complexes)used as bioanalytical tools in various assays over the last three years.We also delved into their detective mechanisms,specific application areas,and characterization tools for responsive behavior.This review aims to showcase the most recent and comprehensive research progress in fluorescent bioanalysis based on molecular and nanoparticle probes,offering guidance for future developments in the design and fabrication of fluorescent probes and their potential applications.

Evidence fusion theory in healthcare

In the context of popularized healthcare,cloud computing centers are used to collect medical data from the cloud and diagnose illnesses.This means a technical framework that can be applied to the medical diagnostic process in popularized healthcare is needed in order to provide technical support.Based on the evidence fusion theory,this study established a multi-modality image evidence fusion method,which can simulate the doctor’s diagnostic process and use multiple modalities of medical images to diagnose illnesses.This study used the evidence fusion method to fuse two different modalities of medical images.The accuracy of the diagnosis after fusion was higher than that of diagnosis through two modalities separately.This fusion method has achieved great results in the process of multi-modality image fusion.