An automated liver tumour segmentation and classification model by deep learning based approaches

Liver cancer is regarded as one of the most common and leading causes of cancer death around the world. Automatic liver tumour segmentation and classification techniques are essential for assisting doctors in the tumour diagnosis process. As artificial intelligence progresses, powerful classification algorithms that can modify a variety of real-world applications are becoming accessible. Due to noise, non-homogeneity and the considerable appearance variability seen in tumour tissue, classifying liver tumours is a difficult undertaking. We have offered a new automatic liver tumour segmentation and classification methodology in this paper. The procedure for detecting liver tumours consists of two steps: first, mask-RCNN (Regions with Convolutional Neural Networks) segmentation of the liver part and then MSER (Maximally Stable Extremal Regions) tumour identification. We have used a hybrid Convolution Neural Network (CNN) model based on deep learning to perform the classification. The segmentation framework attempts to distinguish between normal and malignant tissue in the liver, while the classification method computes multi-class classification of the tumours found. The goal of this research is to come up with an unbiased forecast that is independent of human error. Our proposed method, on the other hand, nearly equals the top segmentation and classifying performance and provides the highest precision for lesion identification while keeping a high recall value. Our proposed approach correctly classifies identified liver tumours into three categories: hepatocellular carcinomas (HCC), malignant (other than HCCs) and benign or cyst with an average accuracy of 87.8%. The novelty of this paper lies in designing a mask-RCNN-based method for segmentation of the liver portion, implementing MSER to segment tumour lesions and using a hybrid CNN-based approach to categorise liver masses.