Abstract:

Objective: The global health system is being shaped by multidisciplinary studies on the diagnosis of diseases and the provision of effective treatment services. Information and communication technologies have been developing laboratory and imaging studies through artificial intelligence-supported systems for the last twenty years. Studies with high accuracy levels in the diagnosis and treatment protocols of diseases make important contributions to making healthy decisions. Artificial intelligence applications have been actively used in the treatment processes of neurological cancer cases in the field of health, as in many fields in recent years. Among these applications, the machine learning model has started to be preferred in the detection of brain tumors because it can provide remarkable results. The main purpose of the study is to provide a supportive analysis for the organization of early diagnosis and rapid treatment in areas such as intracranial pressure, tumor treatment and radiotherapy of patients during intensive care processes. Materials and Methods: In this study, the method developed by doctors with machine learning Kaggle and developers of samples in the network through an example of an application that was developed through machine learning on brain tumors, brain tumor detection carried on with the validation of the data sets includes four classifications. Results: The study consists of two different study systems, namely practice and test. Sectional images from 2865 brain magnetic resonance imaging (MRI )and computed tomography (CT) samples were examined as training in the first stage of the application using the convolutional neural network (CNN) model, and the detected tumors were classified. In this context, MRI results were obtained on 2865 samples with 2470 units and 86.23% with tumors, and 395 units and 13.76% no tumors. Conclusion: In the study, samples with tumors were detected in a 3-month period for brain tumor detection with artificial intelligence and classified typologically. Accordingly, the reliability of the application was proven by providing 98.55% verification on 2865 samples, 3 different tumor types and no tumor data.

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