Brain Tumor Detection and Classification Using ShuffleNet : A Lightweight Deep Learning Approach

Brain tumor detection is an important task in medical imaging and requires accurate and efficient analysis to support early diagnosis and treatment planning. This study presents a deep learning-based system using the Shuffenet V2 algorithm. It is known for its optical architecture and computational efficiency. The proposed system processes MRI scans to recognize and classify brain tumors. That is, the ability to compensate and speed to compensate and speed is harmonious. This is suitable for use in resource-related environments. The workflow includes data preprocessing, model training with marked MRI datasets, and real-time tumor classification. The unique point-to-point group survivors and channel shuffling mechanism of mesh lights allow the model to reduce compensation complexity while simultaneously maintaining robust performance. The integration of segmentation-specific decoders improves the ability of the model to perform the classification of tumor regions within pixels. This approach provides an inexpensive and scalable solution for medical image analysis, offering considerable potential for distant care and undercare environments. This study highlights the effectiveness of shufflenets in coping with medical imaging.