Enhanced classification loss functions and regularization loss function (ECLFaRLF) algorithm for bowel cancer feature classification

Bowel cancer is one of the most common cancers as stated in the bowel cancer cases statistics. The proposed technique is to recognize the pattern of tissue affected by bowel cancer by using support vector machine (SVM) classification. This research aims to increase the accuracy of detecting and classifying bowel cancer with reduced processing time. The proposed method considered a feature extraction and image classification by Eenhanced Classification Loss Functions and Regularization Loss Function (ECLFaRLF) algorithm. This method allowed for more precise interpretations regarding the best associations for bowel cancer. The proposed was tested on colorectal images from different datasets commonly investigated in the proposed solution. The test was evaluated by applying 10-fold cross-validation method. All classification methods provide differentiation rate above processing time 0.413 s, and accuracy 95.67% for the state of the art solution, but by introducing SVM2 classification algorithm produce high accuracy rate with average accuracy is 97.02% over 95.67% and with processing time 0.359 s over 0.413 s. This reality shows the significance of the discriminating power of the SVM2 classifier. The proposed framework has presented an examination of feature extraction and classification techniques to help pathologists in the identifying of benign and malignant diagnosis of bowel cancer.