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@article{183624,
        author = {GOTURI YASHWANTH REDDY and CHIRUMANI PRADYUMNA REDDY and DESA ADITHYA and KONDREDDY NAVA SANDESH REDDY and GUDOORI DHRUV and M S Y K G FRANZONIA and SANAGALA VISHWANATH},
        title = {Automatic Diagnosis of Schizophrenia using Hybrid Neural Networks: A Feature-Driven Study},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {4134-4140},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183624},
        abstract = {Schizophrenia is a chronic and severe mental disor- der that affects how a person thinks, feels, and behaves. It often leads to hallucinations, delusions, disorganized speech, cognitive impairments, and social withdrawal. Affecting approximately
20 million people worldwide according to the World Health Organization (WHO), schizophrenia poses a significant challenge to global mental health care. Despite decades of research, early and accurate diagnosis of schizophrenia remains difficult due to the subjective nature of clinical assessments, symptom overlap with other psychiatric conditions, and variability in individual manifestations. In recent years, the integration of artificial intelligence (AI) and deep learning techniques into the medical domain has shown promise in transforming psychiatric diagnostics. Machine learning models, particularly deep neural networks, have demon- strated the ability to analyze complex patterns in large-scale data, such as neuroimaging scans, genetic data, and behavioral records, offering new opportunities for objective and automated diagnosis.},
        keywords = {Schizophrenia, Deep Learning, CNN, LSTM, Psychiatric diagnosis, Neuroimaging.},
        month = {September},
        }