Neural Precision: Advanced Techniques for Brain Tumor Classification and Detection

In the quest for advancement in Deep Learning techniques, many new principle directions emerged into the light, but this paper is dedicated to usage of computer vision in healthcare, majorly brain tumors. This paper concentrates on enhancing the detection and Classification of neural diseases, with a particular focus on brain tumors. This paper incorporates state-of-the-art techniques such as Gray Level Co-occurrence Matrix (GLCM) algorithms, the concept of skip connections, and the VGG19 model to perform logical and regressive analysis. Additionally, Softmax functions are employed to carry out the Classification task. Certain aspects of upsampling and downsampling data, along with dimensional extension, are also utilized to make the algorithm compatible with the Convolutional Neural Networks (CNN) model. To train the machine to fully understand the complex relationship between the images and classes, the dataset contains 7, 022 images, with 20% of the data used for validation. This paper is distinct in that, unlike many other studies that show considerable variation between testing and validation results, the findings here demonstrate consistent and stable performance. The model achieved an accuracy of 93.4% and reduced the loss to 27.5%.