The formation of a group of abnormal cells in the brain that penetrate the neighboring tissues is known as a brain tumor. The initial detection of brain tumors is necessary to aid doctors in treating cancer patients to increase the survival rate. Various deep learning models are discovered and developed for efficient brain tumor detection and classification. In this research, a transfer learning-based approach is proposed to resolve overfitting issues in classification. The BraTS – 2018 dataset is utilized in this research for segmentation and classification. Batch normalization is utilized in this experiment for data pre-processing and fed to a convolutional layer of CNN for extracting features from Magnetic Resonance Images (MRI). Then, an Adaptive Whale Optimization (AWO) algorithm is utilized to select effective features. This work proposes a Convolutional Neural Network (CNN) based segmentation and a transfer learning-based VGG-16 model for effective classification. The performance of the proposed CNN-VGG-16 technique is analyzed through various tumor regions like TC, ET, and WT. The proposed method attains a Dice score accuracy of 99.6%, 95.35%, and 94%, respectively, when compared to other existing algorithms like CNN, VGG-net, and ResNet. ABSTRAK: Pembentukan gumpalan sel abnormal dalam otak yang menembusi tisu-tisu jiran adalah dikenali sebagai tumor otak. Pengesanan awal tumor otak adalah penting bagi membantu doktor merawat pesakit kanser bagi meningkatkan kadar jangka hayat. Terdapat banyak model pembelajaran mendalam berkaitan kecekapan pengesanan tumor otak dan pengelasan. Dalam kajian ini, pendekatan pembelajaran berdasarkan pindahan dicadangkan bagi mengatasi isu terlebih padan dalam pengelasan. Set data BraTS – 2018 telah digunakan dalam kajian ini bagi tujuan pensegmenan dan pengelasan. Kelompok normal digunakan dalam eksperimen ini bagi data awal proses dan disalurkan kepada jalur lingkaran CNN bagi mengekstrak ciri-ciri dari Imej Resonan Magnetik (MRI). Kemudian, algoritma Optimalisasi Mudah Suai ‘Whale’ (AWO) digunakan bagi memilih ciri-ciri berkesan. Kajian ini mencadangkan Lingkaran Rangkaian Neural (CNN) berdasarkan segmentasi dan model VGG-16 berdasarkan pindahan bagi pengelasan berkesan. Prestasi teknik CNN-VGG-16 yang dicadangkan diuji dengan pelbagai bahagian tumor otak seperti TC, ET dan WT. Kaedah yang dicadangkan ini beroleh ketepatan skor Dice sebanyak 99.6%, 95.35% dan 94% masing-masing jika dibanding dengan algoritma sedia ada seperti CNN, VGG-net dan ResNet.