Abstract
Deep learning is a robust technique that aims to build systems that can improve automatically based on experience using advanced statistical and probabilistic data. It has led to significant advances in the health sector by collaborating health professionals with data scientists that solved challenges previously not possible. Mental illness deeply impacts the day-to-day living of a person and is found to be very common in society these days. However, it is taken for granted in many cases. To overcome this issue, analytical techniques are implemented on mental health data which has shown broad potential in improving patient outcomes and also, to better understand the existence of psychological conditions in a wider community. With the rapidly growing field of bioinformatics, deep learning plays a pivotal role in detection by enabling speedy and scalable analysis of complex data like images of the brain. While this technique can be employed and worked at the practice for the early diagnosis of a mental illness, the same can be extended for real-time monitoring of the individual and improving the efficiency of treatment. This chapter gives an insight into the involvement of Neural Networks in the medical profession along with the comprehension of bioinformatics. Furthermore, the chapter progresses with the practice for diagnosing mental illness like Alzheimer’s, anxiety, depression, schizophrenia, dementia, etc., eventually discussing how deep learning can be implemented in the detection of mental illness. Finally, the chapter summarizes the implications for future work.
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Rajendran, S., Gandhi, R., Smruthi, S., Chaudhari, S., Kumar, S. (2023). Diagnosis of Mental Illness Using Deep Learning: A Survey. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_12
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