Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
Source abstract
Abstract
Breast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven optimization operators, the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA), in an effort to boost classification appreciation and the convergence of the model. The suggested model demonstrated excellent findings as the DOA-optimized MobileNet acquired the highest performance of 98.96 percent accuracy on the fusion test, and the FLA-optimized MobileNet scaled up to 98.06 percent and 95.44 percent accuracies on mammographic and ultrasound tests, respectively. Further to good quantitative results, Grad-CAM visualizations indeed showed clinically consistent localization of the lesions, which strengthened the interpretability and model diagnostic reliability of Grad-CAM. These results show that lightweight, compact CNNs can be used to do high-performance, multimodal breast cancer diagnosis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Brain Tumor Detection and Classification · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Natural and Applied Sciences, Department of Computer Engineering, Gazi University, Ankara 06560, Türkiye
Production Mechanics Techniques, Kirkuk Technical Institute, Northern Technical University, Kirkuk 36001, Iraq
Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, Türkiye
References (71)
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