Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy:A review

Upper gastrointestinal(GI)cancers are the leading cause of cancer-related deaths worldwide.Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy.However,unlike GI cancers,precancerous lesions in the upper GI tract can be subtle and difficult to detect.Artificial intelligence techniques,especially deep learning algorithms with convolutional neural networks,might help endoscopists identify the precancerous lesions and reduce interobserver variability.In this review,a systematic literature search was undertaken of the Web of Science,PubMed,Cochrane Library and Embase,with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract.The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized.The challenges and recommendations targeting this field are comprehensively analyzed for future research.

Clinical Application of Preliminary Breast Cancer Screening for Dense Breasts Using Real-Time AI-Powered Ultrasound with Deep-Learning Computer Vision

Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound video feed,provides BI-RADS categorizations;and has reliable sensitivity and specificity.Multiple deep-learning models were trained on more than 300,000 breast ultrasound images to achieve object detection and regions of interest classification.The main objective of this study was to determine whether the performance of our Al-powered solution was comparable to that of ultrasound radiologists.Methods:The noninferiority evaluation was conducted by comparing the examination results of the same screening women between our AI-powered solution and ultrasound radiologists with over 10 years of experience.The study lasted for one and a half years and was carried out in the Duanzhou District Women and Children's Hospital,Zhaoqing,China.1,133 females between 20 and 70 years old were selected through convenience sampling.Results:The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 93.03%,94.90%,90.71%,92.68%,and 93.48%,respectively.The area under the curve(AUC)for all positives was 0.91569 and the AUC for all negatives was 0.90461.The comparison indicated that the overall performance of the AI system was comparable to that of ultrasound radiologists.Conclusion:This innovative AI-powered ultrasound solution is cost-effective and user-friendly,and could be applied to massive breast cancer screening.

Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging

Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China.Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate,which will reduce medical costs.The current diagnostic methods for early colorectal cancer include excreta,blood,endoscopy,and computer-aided endoscopy.In this paper,research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology,3D modeling,5G remote technology,endoscopic robot technology,and surgical navigation technology.The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques.

Smart MobiNet:A Deep Learning Approach for Accurate Skin Cancer Diagnosis

The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.

Enhanced Diagnostic Precision:Deep Learning for Tumors Lesion Classification in Dermatology

Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized cutaneous malignancies.Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma.The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma.The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions.The present study investigates the feasibility of automated methods for detecting pigmented skin lesions with potential malignancy.To diagnose skin lesions,medical professionals employ a two-step approach.Before detecting malignant types with other deep learning(DL)models,a preliminary step involves using a DL model to identify the skin lesions as either pigmented or non-pigmented.The performance assessments accurately assessed four distinct DL models:Long short-term memory(LSTM),Visual Geometry Group(VGG19),Residual Blocks(ResNet50),and AlexNet.The LSTM model exhibited higher classification accuracy compared to the other models used.The accuracy of LSTM for pigmented and non-pigmented,pigmented tumours and benign classes,and melanomas and pigmented nevus classes was 0.9491,0.9531,and 0.949,respectively.Automated computerized skin cancer detection promises to enhance diagnostic efficiency and precision significantly.

Effect of Image Noise on the Classification of Skin Lesions Using Deep Convolutional Neural Networks

Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Deep Convolutional Neural Networks(CNNs)are now the most prevalent computer algorithms for the purpose of disease classification.As with all algorithms,CNNs are sensitive to noise from imaging devices,which often contaminates the quality of the images that are fed into them.In this paper,a deep CNN(Inception-v3)is used to study the effect of image noise on the classification of skin lesions.Gaussian noise,impulse noise,and noise made up of a compound of the two are added to an image dataset,namely the Dermofit Image Library from the University of Edinburgh.Evaluations,based on t-distributed Stochastic Neighbor Embedding(t-SNE)visualization,Receiver Operating Characteristic(ROC)analysis,and saliency maps,demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.

A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning.While voxel-based deep learning frameworks have been proposed for this segmentation task,their performance remains limited.In this study,we offer a two-step surface-based deep learning pipeline that achieves significantly better results.Our proposed model takes a surface model of an entire set of principal brain arteries containing aneurysms as input and returns aneurysm surfaces as output.A user first generates a surface model by manually specifying multiple thresholds for time-of-flight magnetic resonance angiography images.The system then samples small surface fragments from the entire set of brain arteries and classifies the surface fragments according to whether aneurysms are present using a point-based deep learning network(PointNet++).Finally,the system applies surface segmentation(SO-Net)to surface fragments containing aneurysms.We conduct a direct comparison of the segmentation performance of our proposed surface-based framework and an existing voxel-based method by counting voxels:our framework achieves a much higher Dice similarity(72%)than the prior approach(46%).

Diagnosis and therapeutic strategies for small bowel vascular lesions

Small bowel vascular lesions, including angioectasia (AE), Dieulafoy’s lesion (DL) and arteriovenous malformation (AVM), are the most common causes of obscure gastrointestinal bleeding. Since AE are considered to be venous lesions, they usually manifest as a chronic, well-compensated condition. Subsequent to video capsule endoscopy, deep enteroscopy can be applied to control active bleeding or to improve anemia necessitating blood transfusion. Despite the initial treatment efficacy of argon plasma coagulation (APC), many patients experience re-bleeding, probably because of recurrent or missed AEs. Pharmacological treatments can be considered for patients who have not responded well to other types of treatment or in whom endoscopy is contraindicated. Meanwhile, a conservative approach with iron supplementation remains an option for patients with mild anemia. DL and AVM are considered to be arterial lesions;therefore, these lesions frequently cause acute life-threatening hemorrhage. Mechanical hemostasis using endoclips is recommended to treat DLs, considering the high re-bleeding rate after primary APC cauterization. Meanwhile, most small bowel AVMs are large and susceptible to re-bleeding therefore, they usually require surgical resection. To achieve optimal diagnostic and therapeutic approaches for each type of small bowel lesion, the differences in their epidemiology, pathology and clinical presentation must be understood.

Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images

Deep learning is widely used for lesion segmentation in medical images due to its breakthrough performance.Loss functions are critical in a deep learning pipeline,and they play important roles in segmenting performance.Dice loss is the most commonly used loss function in medical image segmentation,but it also has some disadvantages.In this paper,we discuss the advantages and disadvantages of the Dice loss function,and group the extensions of the Dice loss according to its improved purpose.The performances of some extensions are compared according to core references.Because different loss functions have different performances in different tasks,automatic loss function selection will be the potential direction in the future.

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography

BACKGROUND The accurate classification of focal liver lesions(FLLs)is essential to properly guide treatment options and predict prognosis.Dynamic contrast-enhanced computed tomography(DCE-CT)is still the cornerstone in the exact classification of FLLs due to its noninvasive nature,high scanning speed,and high-density resolution.Since their recent development,convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks.AIM To develop and evaluate an automated multiphase convolutional dense network(MP-CDN)to classify FLLs on multiphase CT.METHODS A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCECT imaging protocol(including precontrast phase,arterial phase,portal venous phase,and delayed phase)from 2012 to 2017 were retrospectively enrolled.FLLs were classified into four categories:Category A,hepatocellular carcinoma(HCC);category B,liver metastases;category C,benign non-inflammatory FLLs including hemangiomas,focal nodular hyperplasias and adenomas;and category D,hepatic abscesses.Each category was split into a training set and test set in an approximate 8:2 ratio.An MP-CDN classifier with a sequential input of the fourphase CT images was developed to automatically classify FLLs.The classification performance of the model was evaluated on the test set;the accuracy and specificity were calculated from the confusion matrix,and the area under the receiver operating characteristic curve(AUC)was calculated from the SoftMax probability outputted from the last layer of the MP-CDN.RESULTS A total of 410 FLLs were used for training and 107 FLLs were used for testing.The mean classification accuracy of the test set was 81.3%(87/107).The accuracy/specificity of distinguishing each category from the others were 0.916/0.964,0.925/0.905,0.860/0.918,and 0.925/0.963 for HCC,metastases,benign non-inflammatory FLLs,and abscesses on the test set,respectively.The AUC(95%confidence interval)for differentiating each category from the others was 0.92(0.837-0.992),0.99(0.967-1.00),0.88(0.795-0.955)and 0.96(0.914-0.996)for HCC,metastases,benign non-inflammatory FLLs,and abscesses on the test set,respectively.CONCLUSION MP-CDN accurately classified FLLs detected on four-phase CT as HCC,metastases,benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.

Significance of Increased CIMT with Coexisting Carotid Plaques in Cerebral White Matter Lesions in Elders

It is very common that increased carotid intima media thickness （CIMT） and carotid plaque coexist in a single subject in elderly patients with white matter lesions （WMLs）. In this study we inves- tigated whether the coexistence of increased CIMT and carotid plaque is more strongly associated with the presence and extent of WMLs than either alone. All patients were classified into 1 of the following 4 groups： without either increased CIMT （I） or carotid plaque （P）： I（-）P（-）; with only increased CIMT： I（＋）P（-）; with only carotid plaque： I（-）P（＋）; and with both increased CIMT and carotid plaque： I（＋）P（＋） The presence and severity of periventricular WMLs （PWMLs） and deep WMLs （DWMLs） were as- sessed and the prevalence of MRI findings by the Cochran-Armitage trend test was calculated. The characteristics of subjects showed that the percentages of patients with increased CIMT and carotid plaque in the DWMLs group and the PWMLs group were significantly higher than those without WMLs group. Both DWMLs and PWMLs were strongly associated with age, carotid plaque and CIMT. Furthermore, the Cochran-Armitage trend test indicated that the prevalence of MRI findings of PWMLs and DWMLs increased in the order of I（-）P（-）〈 I（＋）P（-）〈 I（-）P（＋）〈 I（＋）P（＋） （P〈0.0001）. For the pa- tients with DWMLs, the grades of both I（＋）P（-） and I（＋）P（＋） were increased significantly compared to I（-）P（-） （P〈0.0025, P〈0.05, respectively） without such a difference found in patients with PWMLs. Our results suggested that the coexistence of increased CIMT and carotid plaque is most closely associated with WMLs, and that increased CIMT is associated with the severity of DWMLs, whereas carotid plaque is related to the presence of WMLs.

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images

Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.

DCAU-Net:dense convolutional attention U-Net for segmentation of intracranial aneurysm images

Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography(MRA)is essential for medical auxiliary treatments,which can effectively prevent subarachnoid hemorrhages.This paper proposes an image segmentation model based on a dense convolutional attention U-Net,which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences.The U-Net model serves as a backbone,combining dense block and convolution block attention module(CBAM).The dense block is composed of a batch normalization layer,an randomly rectified linear unit activation function,and a convolutional layer,for mitigation of vanishing gradients,for multiplexing of aneurysm features,and for improving the network training efficiency.The CBAM is composed of a channel attention module and a spatial attention module,improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information.Owing to the large variation of aneurysm sizes,multi-scale fusion is performed during up-sampling,for adaptive segmentation of MRA-acquired aneurysm images.The model was tested on the MICCAI 2020 ADAM dataset,and its generalizability was validated on the clinical aneurysm dataset(aneurysm sizes:<3 mm,3–7 mm,and>7 mm)supplied by the Affiliated Hospital of Qingdao University.A good clinical application segmentation performance was demonstrated.

Classification and Research of Skin Lesions Based on Machine Learning

Classification of skin lesions is a complex identification challenge.Due to the wide variety of skin lesions,doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy.The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention.With the development of deep learning,the field of image recognition has made long-term progress.The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology.In this work,we try to classify seven kinds of lesion images by various models and methods of deep learning,common models of convolutional neural network in the field of image classification include ResNet,DenseNet and SENet,etc.We use a fine-tuning model with a multi-layer perceptron,by training the skin lesion model,in the validation set and test set we use data expansion based on multiple cropping,and use five models’ensemble as the final results.The experimental results show that the program has good results in improving the sensitivity of skin lesion diagnosis.

Incontinentia pigmenti with intracranial arachnoid cyst: A case report

BACKGROUND Incontinentia pigmenti(IP)is a rare X-linked dominant genetic disorder that can be fatal in male infants.It is a disease that affects many systems of the human body.In addition to characteristic skin changes,patients may also have pathological features of the eyes,teeth,and central nervous system.Therefore,the lesions in these systems may be the first symptoms for which patients seek treatment.To date,no cases of IP complicated by intracranial arachnoid cyst(IAC)have been reported.This paper aims to report a case of IP with IAC in order to share the diagnosis and treatment experience of this rare case with other clinicians.CASE SUMMARY An 11-year-old female patient suffered intermittent limb convulsions for five months and was sent to hospital.In the initial stage,the patient was considered to have primary epilepsy.Further investigation of the patient's medical history,physical examination and imaging examination led to the diagnosis of IP combined with intracranial space-occupying lesions,and secondary epilepsy.The patient was treated with craniotomy,and postoperative pathology revealed an IAC.The patient recovered well after craniotomy and had no obvious surgeryrelated complications.During the follow-up period,the patient did not have recurrent epilepsy symptoms.CONCLUSION IP is a multi-system disease that presents with typical skin lesions at birth,but the long-term prognosis of this disease depends on the involvement of systems other than the skin,especially nervous system and ocular lesions.

深度学习技术对颅内外动脉阻塞性狭窄的诊断价值

目的探究基于深度学习(DL)技术对颅内外动脉阻塞性狭窄的诊断价值。方法回顾性分析我院2020年1月至2021年6月疑似急性缺血性脑卒中患者,且在一月内接受CTA和DSA。按患者和血管水平将狭窄程度分为正常、轻度狭窄、中度狭窄、重度狭窄和闭塞,阻塞性狭窄定义为直径狭窄率≥70%。以DSA为参考标准,通过受试者工作曲线(ROC)、敏感性、特异性评价诊断性能。结果在患者水平,DL技术与放射科医师的AUC分别为0.781(敏感性和特异性分别为0.934、0.627)和0.840,差异无统计学意义(P=0.074)。在血管水平,DL技术与放射科医师的AUC分别为0.923(敏感性和特异性分别为0.885、0.962)和0.932,差异无统计学意义(P=0.393)。DL技术分析的中位分析时间(8.67 min)明显短于放射科医师(29.55 min)(P<0.001)。结论DL技术可以准确评估颅外和颅内动脉狭窄,耗时短,有望成为优化风险分层和指导治疗策略的方法。

磁共振脑灌注成像在鉴别颅内肿瘤性病变与非肿瘤性病变的应用研究

目的探讨磁共振脑灌注成像鉴别颅内肿瘤性病变与非肿瘤性病变的价值。方法选取临沂市人民医院2020年2月至2024年2月收治并经病理证实的33例颅内肿瘤患者作为肿瘤组,患者均为星形细胞瘤,按照肿瘤级别分为低级别组(15例)及高级别组(18例),选取同期收治的40例颅内非肿瘤性病变患者作为对照组。经磁共振常规序列及脑灌注成像扫描后,计算出肿瘤的最大脑血流量(CBF)最大脑血容量(CBV)。比较各组别的CBF、CBV值,分析影像学表现。结果肿瘤组中,高级别组的CBF、CBV值均高于低级别组,差异有统计学意义(P<0.05);肿瘤组的CBF、CBV值高于对照组,差异有统计学意义(P<0.05);图像显示,星形细胞瘤、脑炎及脱髓鞘患者的CBF、CBV值高于对侧的正常组织,可见明显高灌注表现。结论当星形细胞瘤表现不典型时,可行脑灌注成像检查,以期提高颅内肿瘤性病变及非肿瘤性病变诊断符合率。出现灌注明显升高时,应考虑肿瘤性病变。

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.