The medical industry has seen a surge in the use of artificial intelligence and machine learning techniques in recent years. Deep learning, the most current development in artificial intelligence technology, has allowed robots to mimic human intelligence in ever-more-complex and self-governing ways. Medical artificial intelligence systems used to primarily rely on experts to instruct computers by translating clinical data into logic rules specific to certain clinical scenarios. More advanced machine learning algorithms pick and balance relevant data to educate themselves to comprehend these principles by using data attributes, such as pixels from medical images or raw data from electronic health records (EHRs). Sorting through a medical image database turned out to be a little difficult. A support vector machine and cable neural network combination can be used to classify brain tumours from non-tumors in the input magnetic resonance pictures. This method of analysis was valued by the fig share dataset, which underwent analysis to produce a high degree of perfection. Properties of a cable neural network drove a multiclass support vector machine.