Mammo-Light: A lightweight convolutional neural network for diagnosing breast cancer from mammography images

Abstract

People of all countries, developed and developing alike endure cancer-related fatal diseases. The rate of breast cancer in females is increasing daily, partly due to ignorance and misdiagnosis in the early stages. Diagnosis of breast cancer accurately during its earlier stages of development can result in proper initial treatment for breast cancer. Artificial intelligence can aid in the acceleration and automation of breast cancer detection. Deep learning is decisive in effectively recognizing and classifying cancer on large datasets of medical images. In this paper, we propose a novel computer-aided classification approach, Mammo-Light for breast cancer prediction. Preprocessing strategies have been utilized to eradicate the noise and enhance mammogram lesions. Photometric augmentation techniques adapted to the preprocessed classes to balance and increase the size of the dataset. After that, a lightweight yet intuitive convolutional neural network is applied to classify breast cancer on the publicly available dataset CBIS-DDSM. For further validation of the proposed approach, we have used the MIAS dataset. Mammo-Light attained a 99.17% and 98.42% test accuracy respectively for CBIS-DDSM and MIAS datasets and outperformed state-of-the-art methods in terms of accuracy and other metrics. Due to being the lightweight model, Mammo-Light performs exceptionally well with fewer parameters and computational time, which can potentially contribute to the field of breast cancer early diagnosis and enable fast treatment.