Diagnosis of Schizophrenia Using Sequential CNN

Schizophrenia is a severe mental health illness that can greatly affect cognitive and social functioning. An early diagnosis is undoubtedly critical for timely intervention and may improve disease outcomes and minimize long-term disablement. This proposed a new approach to diagnosing schizophrenia using Electroencephalogram (EEG) signals, processed by a Sequential Convolutional Neural Network (CNN). From there they utilized the CBB-MIT dataset which has many EEG data, using wavelet to transform the data into fewer noise, and then by extracting some statistical features from the improved data and used it for classification. The final output is a vector passed through the final dense layer with softmax activation we got the probability over the given classes. The model gets 92% accuracy and a loss rate of 9.89%, which is better than old ML methods like SVM (Support Vector Machines) and RF (Random Forest). Unlike traditional approaches that need a lot of feature work, the Sequential CNN finds important patterns on its own, making it more scalable and efficient. The results show great promise, but to guarantee generalizability, additional validation on other datasets and actual clinical trials is required. Additionally, ethical issues need to be taken into account, that include patient data protection and the responsible utilization of AI in the medical decision-making. Future work will focus on integrating multiple diagnostic criteria and developing a user-friendly interface for facilitating clinical adoption. Findings highlight deep learning's role in advancing mental health diagnostics, offering a promising direction for improving schizophrenia detection.