Background:Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca).Methods:Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance.Results:An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques.Conclusions:Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

Image segmentation is a widely used in medical imaging applications by detecting anatomical structures and regions of interest. This paper concerns a survey of numerous segmentation model used in biomedical field. We organized segmentation techniques by four approaches, namely, thresholding, edge-based, region-based and snake. These techniques have been compared with simulation results and demonstrated the feasibility of medical image segmentation. Snake was demonstrated a capability with a high performance metrics to detect irregular shape as carcinoma cell type. This study showed the advantage of the deformable segmentation technique to segment abnormal cells with Dice similarity value over 83%.

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