Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

Abstract

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

“…In this research an innovative dataset was used with the aim of maximizing the efficiency of the colorectal cancer detection system, similar to the work presented in [ 37 ]. This novel dataset composed of the following 33 blood and urine data for each patient: albumin, direct and total bilirubin, creatinine, alkaline phosphatase, gamma GT, glycemia, GOT, GPT, potassium, total protein level, sodium, quick time, PI, INR, urea, iron, leukocytes, basophils, neutrophils count, neutrophils percentage, eosinophils percentage, lymphocytes percentage, monocytes percentage, MCV, hemoglobin percentage, erythrocytes count, MCH, MCHC, RDW (rdw-cv), RDW (rdw-sd), hematocrit and platelet count.…”

Transfer Learning Approach for Classification of Histopathology Whole Slide Images

“…In terms of detection and identification, the ResNet50 system outperformed InceptionV3 and Inception-ResNetV2 with 0.98 accuracy, whereas InceptionV3 attained 0.97, and Inception-ResNetV2 attained 0.87. An ensemble-based framework to classify in vivo endoscopic images as normal or abnormal using VGG, DenseNet, and inception-based networks was proposed [ 44 ]. Sari et al [ 45 ] proposed a semi-supervised classification scheme based on a restricted Boltzmann machines to classify histopathological tissue images.…”

The role of AI technology in prediction, diagnosis and treatment of colorectal cancer

“…It is postulated by Mori et al that the added benefits of incorporating deep learning into colonoscopies, other than the obvious one of increased diagnosis accuracy, comes from minimizing any detection rate variations, allowing for better teaching tools that allow to endoscopists and those training, and to reduce the amount of unnecessary polypectomies (Mori et al 2017 ). Endoscopic studies involved with AI have been blossomed (Table 2 ) (Ichimasa et al 2018 ; Nakajima et al 2020 ; Lai et al 2021 ; Yamada et al 2019 ; Chen et al 2018 ; Repici et al 2020 ; Kudo et al 2020 ; Mori et al 2018 ; Nguyen et al 2020 ; Deding et al 2020 ). Roadblocks to implementation of these technologies include technological progress, the lack of regulations, clinical trials, feasibility, risks of misdiagnosis and others (Kudo et al 2019 ; Mori et al 2017 ).…”