Automated breast cancer mass diagnosis: leveraging artificial intelligance for detection and classification

Breast cancer, a prevalent global concern affecting women, underscores the importance of early detection for improved treatment outcomes and reduced mortality rates. Mammogram image is widely employed as a tool for early detection of breast tumors. Incorrect diagnoses elevate the risk of cancer metastasis to vital organs like the lungs, stomach and lymph nodes. This study presents a software application categorizing mammogram images as benign or malignant. It relies on intrinsic features and employs twelve pre-trained deep-learning models. Additionally, ten feature selection algorithms are utilized to identify crucial attributes. Exploiting various feature selection techniques, pinpoint the most representative ones. The selected features from each algorithm contribute to building and testing the Gaussian Support Vector Machine (SVM) classifier. ReliefF selects the optimal features, reflecting the highest test accuracy in the SVM classifier. The recorded results demonstrate an accuracy, sensitivity, precision and specificity of 99.9%. These findings underscore the potential of combining diverse deep-learning structures with feature-reduction techniques to enhance diagnostic capabilities. The research highlights the technology’s potential adoption in the healthcare sector, particularly considering the substantial volume of images involved and the heightened reliability it introduces to the mammogram image diagnosis process.

Deep learning; Breast cancer; Warper methods; PCA; ICA; Feature selection

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