Liver Tumour Detection and Classification using Image Processing and Machine Learning

The liver plays a crucial role in maintaining overall health by undertaking a multitude of essential functions. Among these duties is the conversion of food into bile and proteins, vital for the process of digestion. The liver aids in eliminating potentially harmful inhaled chemicals from the human body, breaks down a variety of nutrients that are ingested through the digestive tract, and controls the absorption of cholesterol by drawing on minerals, vitamins, and carbs that have been stored. The body tissues are composed of microscopic structures called cells, which undergo proliferation and division in the normal course of events, generating new cells to replace old or damaged ones. Failure to remove tissues from dead or damaged cells can lead to the formation of nodules and tumors.Two kinds of tumors, benign as well as malignant, can be generated by the liver, with malignant tumors presenting a bigger risk to human health. Thisresearch article introduces a methodology for the classification as well as detection of liver cancers, utilizing image processing along with machine learning. The provided approach, detailed here, incorporates the fuzzy histogram equalization method at the pre-processingstage of image creation to disease image noise. Subsequently, the images are segmented into multiple parts to pinpoint the regions of interest. For the classification work at hand, the Support Vector Machine, the ANN technique, as well as the random forest technique are all utilised.