An Enhanced Automated Identification of Brain Tumor Cells Using Image Segmentation

Abstract

Brain tumors are a serious threat to human health, and getting a proper diagnosis quickly is essential for successful treatment. This study introduces a novel automated method that uses improved image segmentation algorithms to detect brain tumor cells in diagnostic images. The work uses cutting-edge deep learning models and novel preprocessing techniques to boost the precision and speed with which tumor cells may be identified. The procedure starts with gathering a large dataset of brain tumor images, then moves on to intensive preprocessing to improve the quality of those images. To accurately define tumor locations, a new picture segmentation approach is developed that utilizes convolutional neural networks (CNNs) and morphological operations. The segmented sections are then used to develop a deep learning classifier to recognize tumor cells. The proposed method has been shown to be effective in experiments, with a noticeable increase in the accuracy of identifying tumor cells over previous methods. On a large test dataset, the system demonstrates clinical viability with an average classification using Average polling technique of 92% and classification using Max polling technique of 98.87%. In conclusion, this study advances medical image analysis by providing a more effective automated method of identifying brain tumor cells.