Paper Title
BREASTVISION: An EfficientNet-Based Deep Learning Framework for Accurate Breast Cancer Detection and Classification
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Registration ID: IJNRD_326418
Published ID: IJNRD2606298
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Keywords
Breast Cancer Classification, EfficientNetB1, Radiomics Feature Extraction, Mammogram Analysis, Deep Learning for Medical Imaging.
Abstract
One big reason women die from cancer around the world is still breast cancer, so tools that help doctors find it earlier must be precise and work automatically. Instead of just adding features together, this research introduces BREASTVISION, which builds on EfficientNetB1 while weaving in radiomic data pulled from mammograms stored in the DDSM archive. To clean up images before analysis, the method first strips away empty space by tracing outer edges based on pixel levels. Noise fades out thanks to smoothing tricks like Gaussian and Median filters doing quiet cleanup behind the scenes. For better visibility, CLAHE lifts flat areas into sharper view without blowing highlights apart. Each scan then gets scaled evenly across brightness levels, helping models stay steady when learning patterns. Rather than wasting effort on blank zones, only tight patches holding actual tissue get picked for closer look. From breast lesion scans, it pulls out many custom-made details by hand. Things like average brightness show up first, along with spread, lopsidedness, and peak sharpness. Texture patterns come next, pulled from gray-scale pixel pairings across space. Shape traits follow - area, outer line length, roundness, tightness, how stretched they are, plus jagged edges at borders appear too. Other clues include border-focused signals, differences between sides, tissue thickness, and tiny calcium spots. At the very same time, deeper markers form through reuse of an existing network model trained widely before. That backbone - EfficientNetB1 - is tuned here without starting fresh, pulling complex image codes all on its own. Mammograms feed into this pipeline so hidden structures emerge automatically. These learned pieces help tell types apart later down the road. Training the classification model involves data augmentation, while adjustments happen through Adam optimization featuring dynamic learning rates alongside early halting to avoid excessive fitting. Metrics like Accuracy, ROC-AUC, and Precision-Recall AUC guide performance checks, supported by confusion matrices and detailed classification summaries. Beyond labels, the system estimates tumor dimensions and includes a suggestion engine offering step-by-step medical insights tied to detected abnormalities. Combining radiomic traits with deep learning outputs strengthens both clarity and reliability, showing promise within automated tools designed for spotting and sorting breast cancer cases.
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How To Cite (APA)
BHAGAVATULA SRI RAM VISHNU, DR. CH. RAMESH BABU, & DR. K. SATISH KUMAR (June-2026). BREASTVISION: An EfficientNet-Based Deep Learning Framework for Accurate Breast Cancer Detection and Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(6), c946-c953. https://ijnrd.org/papers/IJNRD2606298.pdf
Issue
Volume 11 Issue 6, June-2026
Pages : c946-c953
Other Publication Details
Paper Reg. ID: IJNRD_326418
Published Paper Id: IJNRD2606298
Research Area: Other area not in list
Author Type: Indian Author
Country: Hyderabad, Medchal, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2606298.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2606298
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