Impelementasi Algoritma Convolutional Neural Network (CNN) Untuk Mendeteksi Penyakit Tumor Otak Berbasis Web

Abstrak

A brain tumor is the growth of abnormal cells in or around the brain unnaturally and
uncontrollably. Diagnosis of brain tumors, such as Glioma, Pituitary, and Meningioma,
often faces obstacles due to subjectivity and uncertainty in the interpretation of medical
images. In addition, visual similarities between tumor types make classification even more
difficult. In Indonesia, brain tumor cases continue to increase, with around 300 patients
diagnosed each year, including children. Conventional diagnostic methods such as biopsy
take a long time, while direct observation by a doctor carries the risk of error. Therefore,
more objective and accurate technology-based solutions are needed. Deep learning using
the Convolutional Neural Network (CNN) method is a potential solution for classifying
and diagnosing brain tumors using medical images. CNN has the ability to extract complex
features automatically and efficiently, so it can increase the accuracy of brain tumor
detection with a low error rate. Several studies have been carried out to develop a CNNbased
system to help health workers identify brain tumors. Based on these problems, this
research aims to implement the CNN algorithm in detecting brain tumors based on websites. It is hoped that this system can be a tool for doctors and the public in obtaining
faster, more accurate and effective diagnosis results