Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform - Simeon Yuda Prasetyo; Ghinaa Zain Nabiilah (Jurnal Teknologi Terpadu, 2023)
Brain tumors are abnormal tissue growths characterized by excessive cell growth in certain brain parts. One of the reliable techniques currently available to identify brain tumors is using Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are monitored and examined for tumor detection by a specialist. Developing more effective and efficient tools to help medical professionals identify brain tumors is urgent as the number of people suffering from brain tumors soars, and the death rate will reach 18,600 in 2021. In previous research, machine learning-based models demonstrated the ability to detect brain tumors with a classification accuracy of 92%, and this result is reliable. We computationally tested several hyperparameters using publicly available MRI datasets to obtain the most reliable binary classification accuracy in MRI brain images. A high level of model accuracy is achieved by testing various existing machine-learning model architectures and inserting a feature map extracted from the Discrete Cosine Transform (DCT). Classification of MRI images achieved the highest accuracy on test data at 93% using the Support Vector Machine (SVM) model.