Convolutional Neural Network-Based Automatic Classification of
Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System
Development Study
- URL: http://arxiv.org/abs/2301.13151v1
- Date: Mon, 30 Jan 2023 18:28:25 GMT
- Title: Convolutional Neural Network-Based Automatic Classification of
Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System
Development Study
- Authors: Remy Peyret and Duaa alSaeed and Fouad Khelifi and Nadia Al-Ghreimil
and Heyam Al-Baity and Ahmed Bouridane
- Abstract summary: We propose a CNN model for classifying colorectal and prostate tumors from multispectral images of biopsy samples.
Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively.
The proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images.
- Score: 7.566742780233967
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Colorectal and prostate cancers are the most common types of cancer in men
worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a
histological analysis on needle biopsy samples. This manual process is
time-consuming and error-prone, resulting in high intra and interobserver
variability, which affects diagnosis reliability. This study aims to develop an
automatic computerized system for diagnosing colorectal and prostate tumors by
using images of biopsy samples to reduce time and diagnosis error rates
associated with human analysis. We propose a CNN model for classifying
colorectal and prostate tumors from multispectral images of biopsy samples. The
key idea was to remove the last block of the convolutional layers and halve the
number of filters per layer. Our results showed excellent performance, with an
average test accuracy of 99.8% and 99.5% for the prostate and colorectal data
sets, respectively. The system showed excellent performance when compared with
pretrained CNNs and other classification methods, as it avoids the
preprocessing phase while using a single CNN model for classification. Overall,
the proposed CNN architecture was globally the best-performing system for
classifying colorectal and prostate tumor images. The proposed CNN was detailed
and compared with previously trained network models used as feature extractors.
These CNNs were also compared with other classification techniques. As opposed
to pretrained CNNs and other classification approaches, the proposed CNN
yielded excellent results. The computational complexity of the CNNs was also
investigated, it was shown that the proposed CNN is better at classifying
images than pretrained networks because it does not require preprocessing.
Thus, the overall analysis was that the proposed CNN architecture was globally
the best-performing system for classifying colorectal and prostate tumor
images.
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