ArticleComputational and mathematical methods in medicine2022
Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.
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 It has been retracted, and should not be counted.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 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed.
- Automated breast cancer diagnosis via intensity-guided contour segmentation and ResNet-based classification using local intensity pattern tensors.Scientific reports · 2025Article
- MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks.Frontiers in computational neuroscience · 2025Article
- The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review.Przeglad gastroenterologiczny · 2024Review
- Efficient lung cancer detection using computational intelligence and ensemble learning.PloS one · 2024Article
- Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care.AIMS public health · 2023Review
- Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images.BioMed research international · 2023Article
- Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer.Open life sciences · 2023Article
- Retracted: Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.Computational and mathematical methods in medicine · 2023Article
- Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN.Sensors (Basel, Switzerland) · 2022Article
- Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms.International journal of molecular sciences · 2022Review
- ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides.Scientific reports · 2022Article
- Lung Cancer Classification and Prediction Using Machine Learning and Image Processing.BioMed research international · 2022Article
- Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease.Computational intelligence and neuroscience · 2022Article
- Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.BioMed research international · 2022Article
- Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor.BioMed research international · 2022Article
- Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.BioMed research international · 2022Article
- Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-07-12Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/D070F54A99806AAACB9BD431999205
- Retracted
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.
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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.