CNN-Based Fashion-MNIST Classification with Insights Toward AI-Based Mental Health Detection

The paper describes a Convolutional Neural Network which was trained on Fashion-MNIST with an accuracy of 89.57 (F1=0.90) on test with a three-block architecture of 32, 64 and 64 convolutional filters, using BatchNormalization and Dropout regularization, trained in 15 epochs using the Adam optimizer. The results of the experiment are rigorously applied to the field of AI-based mental health detection - among the most important and most actively evolving uses of deep learning in the health care industry around the world. More than one billion individuals have mental health disorders among them (WHO, 2022), and only the COVID-19 pandemic caused a 28% (193 to 246 million) and 25% (298 to 374 million) increase in depression and anxiety, respectively (PMC, 2024). CNNs are being actively used to detect mental health in three directions: facial expression recognition at depression and anxiety classification ( CNN+MDNet+ViT ensemble, PMC, 2024); speech spectral analysis at 93.5% depression severity grading and 91.2% depression episodes (PMC, 2024); and social media text classification with AI at 93.5% suicidal ideation and The CNN classification abilities verified on Fashion-MNIST include spatial feature hierarchy extraction, multi-class discrimination, softmax confidence estimation, and comprise the visual grounding layer of all three pathways. Privacy-preserving federated mental health AI, multimodal fusion, culturally adaptive models, longitudinal monitoring, and regulatory compliance are five of the future areas of research under the EU AI Act (2024).