BEES: Bio-Inspired Enhanced Emotional Segmentation System for Depression Detection using Deep Learning CNN with Bee Colony Optimization
Year: 2024
Type: article
Abstract: Growing number of cases of depression globally has encouraged AI researchers to investigate automated ways for timely diagnosis. In this paper we introduce the BEES (Bio-inspired Enhanced Emotional Segmentation System), a novel approach for depression detection from image data. This includes BCO-based emotional region partitioning and swarm inspired optimization algorithms to fine-tune CNN hyperparameters. Together with swarm-based voting for ensemble learning that increases prediction accuracies. Training and Evaluation. The FER-2013 dataset contains 30,524 facial emotion images. The optimized CNN model had an accuracy of 90.45%, and with ensemble enhanced to a final predictive performance of (92.15%) together with AUC;0.96%. The results show the excellent detect indicator of depression which is suitable by using our proposed method, and it proved that bio-inspired algorithms should be used in convolutional-based DL with clinical supervised data for mental health screening. Challenges like feature extraction, convergence and overfitting are solved by the presented framework which makes it a strong solution for scalable real-time depression detection.
Source: Nanotechnology Perceptions
Authors: T. Srajan Kumar, Dr. M. Narayanan, Dr. Harikrishna Kamatham
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404010383