The abnormal formation of the group of cells is known as the tumours. These should be identified as soon as possible to save the human life. This is an imminent risk for human life nowadays. Early identification of such tumours also helps to increase the probability of efficacious treatment and the survivorship of the affected humans. While handling the meticulous findings, the dissimilarity between the realm and the tumour morphology is confused. Lately, some advancements have been noticed in using computer-aided detection (CAD) models. It also improves the efficacy in distinguishing the different kinds of cancer cells, which can be found in the lungs, skin, brain, breast etc. Different types of imaging approaches like mammography, dermoscopy, magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT) are used to determine the tumour cells. All the above-mentioned approaches help in explaining the complications and concerns of the tumours. The goal of this work is to do extensive research about the recent techniques that are all used for the different organs. This research mainly focuses on skin, lungs, breast and brain. For the furnishing of the medical imaging domain, deep learning technology like convolutional neural network (CNN) is widely used. This delivers precise reliability and accuracy. By combining this technology with the CAD systems in the medical domain, the tumour cells are identified in the early stage. By doing this, human errors can be minimised. It is also easy to diagnose the cells precisely.