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

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Computers, Materials & Continua 2024年第5期79卷 1875-1901页

作者： K.Umapathi S.Shobana Anand Nayyar Judith Justin R.Vanithamani Miguel Villagómez Galindo Mushtaq Ahmad Ansari Hitesh Panchal Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Graduate School Faculty of Information TechnologyDuy Tan UniversityDa Nang550000Viet Nam Department of Biomedical and Instrumentation Engineering Avinashilingam Institute for Home Science and Higher Education forWomenCoimbatore641043India UniversidadMichoacana de SanNicolás de HidalgoAvenida Francisco J.Múgica S/N Ciudad Universitaria MoreliaCP58030Mexico Department of Pharmacology and Toxicology College of PharmacyKing Saud UniversityRiyadh11451Saudi Arabia Department of Mechanical Engineering Gujarat Technological UniveristyNigam NagarChandkhedaAhmedabadGujarat382424India

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.

关键词： ultrasound images breast cancer tumor classification segmentation deep learning lesion detection

Ovarian-adnexal reporting and data system ultrasound evaluation and pathological characteristics of ovarian collision tumor

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World Journal of Clinical Cases 2024年第22期12卷 4932-4939页

作者： Chan Yin Yong Wang Zhi-Hui Fei Li-Hong Sun Wei-Ai Zhou Heng Li Department of ultrasound Hunan University of Arts and Sciences Affiliated Changde Maternal and Child Health HospitalChangde 415000Hunan ProvinceChina Department of ultrasound People's Hospital of Shimen CountyChangde 415300Hunan ProvinceChina Department of ultrasound Medicine The Maternal and Child Health Hospital of ChangdeChangde 415000Hunan ProvinceChina Department of Psychology The Maternal and Child Health Hospital of ChangdeChangde 415000Hunan ProvinceChina Department of Gynaecology Hunan University of Arts and Sciences Affiliated Changde Maternal and Child Health HospitalChangde 415000Hunan ProvinceChina Department of ultrasound The First People's Hospital of ChangdeChangde 415000Hunan ProvinceChina

BACKGROUND Collision tumor are neoplasms,including two histologically distinct tumors that coexist in the same mass without histological *** incidence of collision tumor is low and is rare *** To investigate ultrasound images and application of ovarian-adnexal reporting and data system(O-RADS)to evaluate the risk and pathological characteristics of ovarian collision *** This study retrospectively analyzed 17 cases of ovarian collision tumor diagnosed pathologically from January 2020 to December *** clinical features,ultrasound images and histopathological features were collected and *** O-RADS score was used for *** O-RADS score was determined by two senior doctors in the gynecological ultrasound *** with O-RADS score of 1-3 were classified as benign tumors,and lesions with O-RADS score of 4 or 5 were classified as malignant *** There were 17 collision tumors detected in 16 of 6274 patients who underwent gynecological *** average age of 17 women with ovarian collision tumor was 36.7 years(range 20-68 years),in whom,one occurred bilaterally and the rest occurred *** average tumor diameter was 10 cm,of which three were 2-5 cm,11 were 5-10 cm,and three were>10 ***(29.4%)tumors with O-RADS score 3 were endometriotic cysts with fibroma/serous cystadenoma,and unilocular or multilocular cysts contained a small number of parenchymal ***(64.7%)tumors had an O-RADS score of 4,including two in category 4A,six in category 4B,and three in category 4C;all of which were multilocular cystic tumors with solid components or multiple papillary ***(5.9%)tumor had an O-RADS score of *** case was a solid mass,and a small amount of pelvic effusion was detected under *** pathology was high-grade serous cystic cancer combined with cystic mature *** were nine(52.9%)tumors with elevated serum carbohydrate antigen(CA)125 and two(11.8%)with

关键词： Ovarian collision tumor Ovarian-adnexal reporting and data system Epithelial tumor Serous cystadenoma ultrasound images

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

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Computers, Materials & Continua 2021年第3期66卷 3161-3182页

作者： Ihsan Jasim Hussein M.A.Burhanuddin Mazin Abed Mohammed Mohamed Elhoseny Begonya Garcia-Zapirain Marwah Suliman Maashi Mashael S.Maashi BIOCORE Research Group Universiti Teknikal Malaysia MelakaMelaka76100Malaysia Director of UTeM International Centre BIOCORE Research GroupUniversiti Teknikal Malaysia MelakaMelaka76100Malaysia College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates eVIDA Laboratory University of DeustoBilbao48007Spain Medical Laboratory Science Department King Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of ***,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound *** main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer *** addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion *** study uses two databases in training and testing the proposed segmentation *** breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in *** of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound *** segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data *** the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

关键词： Viola-Jones model breast cancer segmentation ovarian tumor ovarian tumor segmentation breast cancer ultrasound images active contour cascade model

Classification of a Small-data-set Thyroid Nodules Based on Supplementary Feature Layer Improved VGG16

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Classification of a Small-data-set Thyroid Nodules Based on ...