Abstract
Simple SummaryColorectal cancer represents one of the major health problems due to high incidence and mortality rates. A diversity of treatment options as well as a rising population require novel diagnostic tools. The main goal of the research was to develop a novel complex colorectal cancer decision support system based on artificial intelligence. The developed system can classify eight classes of tissue and can identify the malignant areas. In order to allow the easiest and most intuitive interaction with clinicians, the corresponding application was also built.Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.
Highlights
Colorectal cancer (CCR) is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually) [1], which provides an important and extensive source of information to help medicine advance
It is noted that the increase did not improve performance, but there are notable differences between the model with the parameters resulting from the training on the “ImageNet” set [24] and the one in which the parameters were obtained from the training realised only on the “Kvasir” set [30]
It can be observed that the metric we are mainly interested in, sensitivity, is about 3–4% higher than in previous cases, even when it was not pre-trained on the “ImageNet” set [24]
Summary
Colorectal cancer (CCR) is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually) [1], which provides an important and extensive source of information to help medicine advance. It represents 10% of the cancer mortality rate each year [2]. Cancer-related death has increased by 45% in recent years and there is a high chance that the rate will rise to 60% over the 15 years [2]. It is estimated that the market value for colon cancer therapies will reach as high as $11 billion by year 2025 due to the increase of branded therapies
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