Automated Detection of Breast Cancer using Artificial Intelligence Systems

The second leading cause of death for women worldwide remains breast cancer. In particular as they age, all women have a higher risk of acquiring breast cancer. Populations range in their incidence of various forms of breast cancer, and they also differ in their risks of developing breast cancer in general. Hence, it is crucial to determine the degree of the malignancy as soon as possible. Artificial intelligence may be able to classify breast cancer with competence levels on par with those of a doctor. This paper proposes the detection of breast cancer through a deep learning algorithm-based approach that involves segmentation, feature extraction, and grading. A soft computing technique is developed to categorize tumours as benign or malignant using breast histopathology images. To determine the exact grades of breast cancer, the initiation technique uses K-mean clustering-based segmentation, feature extraction, and classification. The segmented region yielded attributes such as correlation, homogeneity, contrast, LBPs (local binary patterns), and energy coefficients. In order to establish which network is better at recognizing the diagnosis of breast malignancy, the Artificial neural network classifier, deep learning architectures of AlexNet and GoogLeNet are contrasted. The AlexNet classification methodology produces accurate solutions than other methods by obtaining 100% accuracy. The proposed enhanced segmentation and classification techniques may help the automated diagnosis system to reduce the number of erroneous diagnoses and increase prediction performance. It could therefore aid clinicians in obtaining an additional opinion and in the early detection of diseases.