ArticleComputational and mathematical methods in medicine2022
Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- An Automated Hybrid Deep Learning-based Model for Breast Cancer Detection using Mammographic ImagesCurrent medical imaging · 2026Article
- Automated breast cancer diagnosis via intensity-guided contour segmentation and ResNet-based classification using local intensity pattern tensors.Scientific reports · 2025Article
- Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.Journal of imaging informatics in medicine · 2024Article
- Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs.Scientific reports · 2024Article
- Presegmenter Cascaded Framework for Mammogram Mass Segmentation.International journal of biomedical imaging · 2024Article
- Requirement of image standardization for AI-based macroscopic diagnosis for surgical specimens of gastric cancer.Journal of cancer research and clinical oncology · 2023Article
- Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images.Scientific reports · 2023Article
- BCDiagnostics (Basel, Switzerland) · 2023Article
- A divide and conquer approach to maximise deep learning mammography classification accuracies.PloS one · 2023Article
- Automated Cognitive Health Assessment Based on Daily Life Functional Activities.Computational intelligence and neuroscience · 2023Article
- Enhanced YOLOv5 network-based object detection (BALFilter Reader) promotes PERFECT filter-enabled liquid biopsy of lung cancer from bronchoalveolar lavage fluid (BALF).Microsystems & nanoengineering · 2023Article
- Retracted: Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network.Computational and mathematical methods in medicine · 2023Article
- A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor.Life (Basel, Switzerland) · 2022Article
- Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers.Diagnostics (Basel, Switzerland) · 2022Article
- Breast Cancer Prediction Empowered with Fine-Tuning.Computational intelligence and neuroscience · 2022Article
- Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning.Frontiers in medicine · 2022Article
- Discovery of Knowledge in the Incidence of a Type of Lung Cancer for Patients through Data Mining Models.Computational intelligence and neuroscience · 2022Article
- Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).Computational intelligence and neuroscience · 2022Article
- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
- Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach.Frontiers in genetics · 2022Article
Corrections and comments
- Retraction · 2023-06-28Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies' major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.