Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-04, Vol.22 (8), p.2988
• Main Authors: Vulli, Adarsh, Srinivasu, Parvathaneni Naga, Sashank, Madipally Sai Krishna, Shafi, Jana, Choi, Jaeyoung, Ijaz, Muhammad Fazal
• Format: Article
• Language: English
• Subjects: Applications programs; Automation; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; Cancer; Classification; computational histopathology; DenseNet-169; Digital imaging; FastAI; Female; Health aspects; Humans; Image processing; Image Processing, Computer-Assisted - methods; lymph nodes; Lymph Nodes - pathology; Lymphatic Metastasis - pathology; Lymphatic system; Machine learning; Medical imaging; Medical imaging equipment; Medical research; Metastasis; Microscopy; Mortality; Mutation; Neural networks; Policy; Prevention; Prostate; Ultrasonic imaging; whole-slide images; Womens health
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22082988

Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably.