An Artificial Intelligence Model for Early Stage Breast Cancer Detection from Biopsy Images
This addresses the need for non-invasive and timely classification of breast cancer types to reduce patient burden and treatment delays, though it appears incremental as it builds on existing CNN methods.
The paper tackles the problem of identifying breast cancer types from biopsy images to guide treatment, presenting an AI model that achieves reliable classification performance and outperforms existing solutions in accuracy, precision, recall, and F1-score.
Accurate identification of breast cancer types plays a critical role in guiding treatment decisions and improving patient outcomes. This paper presents an artificial intelligence enabled tool designed to aid in the identification of breast cancer types using histopathological biopsy images. Traditionally additional tests have to be done on women who are detected with breast cancer to find out the types of cancer it is to give the necessary cure. Those tests are not only invasive but also delay the initiation of treatment and increase patient burden. The proposed model utilizes a convolutional neural network (CNN) architecture to distinguish between benign and malignant tissues as well as accurate subclassification of breast cancer types. By preprocessing the images to reduce noise and enhance features, the model achieves reliable levels of classification performance. Experimental results on such datasets demonstrate the model's effectiveness, outperforming several existing solutions in terms of accuracy, precision, recall, and F1-score. The study emphasizes the potential of deep learning techniques in clinical diagnostics and offers a promising tool to assist pathologists in breast cancer classification.