AI SYSTEM FOR BRAIN MRI/CT MALIGNANCY IDENTIFICATION AND CLASSIFICATION USING MODIFIED CNN AND ADAM OPTIMIZATION

• Main Authors: Chacko, Susamma, Al Bimani, Ali, Mohamed Musthafa, Mohamed Essa, Bhupathyraaj, Mullaicharam, Ahamed, Suhail, Priya, R. Krishna, Rajasekaran, M. Pallikonda
• Format: Patent
• Language: English
• Published: 09.12.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

TITLE OF THE INVENTION: "AI SYSTEM FOR BRAIN MRI/CT MALIGNANCY IDENTIFICATION AND CLASSIFICATION USING MODIFIED CNN AND ADAM OPTIMIZATION." The lifestyle of the people and genetic changes in the body and many other factors cause an increase in neurologic diseases in human that in turn may lead to a life threatening state of human. Due to this scenario, the patients suffering from these neurological diseases struggle to get back to good health. This further points to diagnosis of the exact in the human brain. Even though there have been advancements in the neuro imaging techniques, the proper identification and location of the malignancy and in brain still remains as a challenging task. As to rectify or to reduce the problem, artificial intelligence could play a vital role in classification of images from a set of MRI/CT. Different kinds of methods are normally followed, that has the limitations in accuracy and diagnostic time. Therefore, Artificial Intelligence with Deep learning (DL) is enabled to process the MRI/CT data of a subject. The process of Deep learning verifies the data for classification, the optimize the parameter learning rate and selects the optimization method. In this DL, the modified CNN with Yolo v2 method is chosen to delivery better accuracy in the diagnosis where the marking of the malignancy can be performed in this method. The proposed method uses ADAM (Adaptive Moment estimation) for optimization purposes. The classification can be performed based on the modified CNN transfer learning algorithmic system also, which is capable of searching the random data, and could offer efficiency with acceptable accuracy and with reduced diagnostic time. Page 1 of 1 FIGURE 3: THE PROPOSED SYSTEM MODEL Brain Input Data Data Collection and Sorting Pre-Processing Data f Normalization, Shuffling, Resizng SplitData Training and Validation Modified-ConvolutionalNN Optimization by AGDM Training the layer s and parameters method of CNN's Mod-CNN jgMalignancy Complete the training with marking marking Brain Malignancy Evaluating the Validation Data and Testing