Automatic Classification of Breast Cancer Images Using Transfer Learning on Enhanced Mammography Images

Abstract :

Breast cancer is considered one of the major concerns in global health, and it is divided into two types: benign and malignant. The malignant type poses a higher risk due to its faster metastasis. Therefore, there is a critical need for fast and accurate detection. Despite the expertise of radiologists, errors due to incorrect interpretation often lead to misdiagnoses. To address this issue, this paper proposes an intelligent system for analyzing mammography images, which includes preprocessing, feature extraction, and classification stages. In this system, the image quality is first improved using preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE), and then the region corresponding to the cancerous mass is extracted using Otsu’s thresholding segmentation method. Additionally, key features for distinguishing between benign and malignant tumors are extracted using two pre-trained Convolutional Neural Network (CNN) models, namely ResNet50 and InceptionV3. Finally, the extracted features are analyzed using a Support Vector Machine (SVM) classifier to predict the tumor types. The result of this work is an improvement in diagnostic accuracy and early breast cancer detection, which reduces human error and the current challenges in interpreting mammography images

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