Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Jan 23, 2021
Date Accepted: Dec 11, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Convolutional Neural Network based Automatic Classi cation of Colorectal and Prostate Tumour Biopsies using Multispectral Imagery
ABSTRACT
To diagnose colorectal and prostate cancer { the third and second most common cancers among men [1]. { a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone. The use of computer-based quantitative analysis to the human qualitative interpretation can highly reduce intra- and inter-observer variability [2]. In this paper we propose an algorithm (based on VGG16 [3] ) for classi cation of colorectal and prostate tumours from multispectral images of biopsy samples. The key idea is based on removing the last block of convolutional layers and halved the number of lters per layer . Two datasets (one for prostate and one for colorectal tumour biopsy images) were employed to evaluate the proposed CNN architecture. The results show excellent performances when compared to pretrained CNNs and to other classi cation methods as it avoids the preprocessing phase while using a single CNN model for the whole classi cation task. Overall the proposed CNN architecture was globally the best performing system for classifying colorectal and prostate tumour images.
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