Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques. (November 2022)

• Record Type: Journal Article
• Authors: Civit-Masot, Javier; Bañuls-Beaterio, Alejandro; Domínguez-Morales, Manuel; Rivas-Pérez, Manuel; Muñoz-Saavedra, Luis; Rodríguez Corral, José M.
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: Deep learning -- Explainable deep learning -- Convolutional neural networks -- Lung cancer -- Histopathology; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107108
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Lung cancer is the most lethal and the second with the highest number of new cases. A tissue biopsy analysis may be delayed due to the pathologists' workload saturation. Convolutional Neural Networks are commonly used to develop automatic classifiers. The final proposed system improves on the results and reliability of previous work. Explainable Deep Learning techniques help the pathologist to understand the results. Abstract: Background: Lung cancer has the highest mortality rate in the world, twice as high as the second highest. On the other hand, pathologists are overworked and this is detrimental to the time spent on each patient, diagnostic turnaround time, and their success rate. Objective: In this work, we design, implement, and evaluate a diagnostic aid system for non-small cell lung cancer detection, using Deep Learning techniques. Methods: The classifier developed is based on Artificial Intelligence techniques, obtaining an automatic classification result between healthy, adenocarcinoma and squamous cell carcinoma, given an histopathological image from lung tissue. Moreover, a report module based on Explainable Deep Learning techniques is included and gives the pathologist information about the image's areas used to classify the sample and the confidence of belonging to each class. Results: The results show a system accuracy between 97.11 and 99.69%, depending on the number of classes classified, and a value of the area under ROC curve between 99.77 and 99.94%. Conclusions: The classification results obtain a substantial improvement according to previous works. Thanks to the given report, the time spent by the pathologist and the diagnostic turnaround time can be reduced.