NEUROSEGNET: A PANOPTIC SEGMENTATION METHOD BASED ON DEEP LEARNING AND BIG DATA PROCESSING FOR THE IDENTIFICATION OF SPECIFIC NEUROLOGICAL CONDITIONS

Our work's main goal is to render a deep panoptic segmentation model specifically for medical image analysis, with an emphasis on recognizing common neurological disorders. Our model seeks to precisely identify and categorize various regions in brain scans through the integration of sophisticated segmentation technique with Autoencoder based deep Neural Network, thereby aiding in the identification and diagnosis of Neurodegenerative neurological disorders. This method could lead to more successful patient outcomes in neurological healthcare by increasing the accuracy of medical image interpretation. The proposed methodology is geared towards furnishing precise and efficient automated detection and segmentation of neurological irregularities, encompassing lesions, in medical imagery using brain scan images. This innovative approach holds significant promise in revolutionizing the landscape of medical image analysis, offering valuable support to healthcare practitioners in their diagnostic and therapeutic undertakings in web-based form for neurological disorders. The proposed model is designed to support healthcare diagnosis by providing a reliable and effective system that enables automatic recognition and categorization of neurological disorders using imaging techniques of the brain. By leveraging specially designed U-NeuroSegNet infused with big data Spark processing, we achieved exceptional accuracy and efficiency in identifying neurological abnormalities. In the proposed NeuroSegNet, the work focuses to contribute significantly to advancements in neuro-oncology and personalized patient care, ultimately benefiting individuals affected by neurological disorders. Our study utilized big datasets consisting of brain scan images. Our NeuroSegNet model achieved F1-score of 95.6%, a high accuracy of 98.2%, precision of 97.8%, sensitivity of 93.7% and recall rate of 98.0. These results demonstrate the NeuroSegNet models effectiveness in accurately detecting Neurological disorders, lesions and tumors.