Brain cancer is one of the most sophisticated health challenges; early diagnosis is quite critical to attempting or ensuring a relatively good prognosis. Traditional diagnostic.
methods generally rely on manual interpretation of MRI images, a process that can be quite time-consuming and generally predisposed to human errors. This paper will outline
the role of deep learning, in particular, the convolutional neural network methodology, in transforming brain cancer diagnosis and treatment. Advanced VGG16, VGG19, and U-Net models in the present work provide very remarkable improvements of the braintumor classification and segmentation. It reaches an accuracy rate as high as 99.178% for the classification task and 93.8% for segmentation. Deep learning not only enhances the
precision of the diagnosis but also contributes to the development of more personalized treatment plans and, finally, improves the health outcomes of the patients.
It does contribute by taking advantage of advanced ethnologies within the scope of medical imaging dealing with problems created by brain cancer. Additionally, this work contributes to how deep learning may change the future of treatment related to cancer.
Brain cancer segmentation and classification using deep learningيمثل البحث الذي يحمل عنوان
محاولة لتسخير التقنيات الحديثه خصوصاً خوارزميات التعلم العميق لمحاولة تحسين الرعاية الطبية و توفير ادوات تقنية حديثه تفهم صور الرنين المغناطيسي MRI للمخ و تقوم بتشخيصها و تحديد حدود الورم كما يفعل الطبيب و لكن بسرعه و دقة اكبر .