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Cancer diagnosis and treatment has a great significance due to the prevalent episodes of the diseases, high death rate and reappearance after treatment.. On the world scale, cancer stands in the fifth position which causes death. Among the various cancers, liver cancer stands in the third position. Liver cancer is generally diagnosed by three different test like blood test, image test and biopsy. To make the task of detecting the liver cancer simpler, less time consuming, an effective and efficient approach is adopted for the same. In this research a computer aided diagnostic system for detecting liver cancer is put forward. The proposed detection methodology makes use of MRI, CT and USG scan imagery. Kmeans clustering technique is adopted so as to segment the images in order to capture the region of interest. Later, Haar wavelet transform is considered to compute the threshold values for the region of interest. The experiment put forth gave an average accuracy of 82% besides reducing the time complexity and computational complexity of the test.

Breast Cancer is one of the most dreadful diseases and is a potential cause of death in women. Late prediction of Breast Cancer may greatly reduce survival chances, and as a solution to that the automatic disease detection system aids the medical field to diagnose and analyze, which offers rapid response, reliability, effectiveness as well as decrease the risk of death. In this paper, we explain how breast cancer can be predicted using a Machine Learning Technique named Random Forest Classifier. This classifier structures the data into numerous trees and obtains a final result i.e., whether a person is at risk of having breast cancer or not. This model has an accuracy of 98%.

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