RETRACTED ARTICLE: Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process.

This paper presents the design and development of a multimodal engine for preemption and post-treatment recommendations for skin diseases using a deep learning-based hybrid bioinspired process. The proposed system integrates multiple modalities, including image analysis, patient sleep patterns, eating habits, and other medical parameters, to provide personalized and accurate recommendations for the prevention and treatment of skin diseases. The system is based on a novel hybrid bioinspired process that combines elephant herding optimization (EHO) with antlion optimization (ALO) to improve the accuracy and robustness of the recommendations. The system employs a binary cascaded convolutional neural network (BCCNN) for skin image analysis and natural language processing (NLP) with pattern analysis for patient history analysis. The system's architecture comprises multiple modules, including a data preprocessing module, a feature extraction module, a multimodal fusion module, and a recommendation generation module, that are tuned by the bioinspired optimization process. The system was trained and evaluated on a large dataset of skin disease images and patient histories. The results demonstrate that the proposed system outperforms existing state-of-the-art methods by 4.5% in terms of accuracy, 3.2% in terms of precision, 5.9% in terms of recommendation recall, and 1.4% in terms of AUC (area under the curve) metrics, when evaluated on clinical data samples. The proposed system has the potential to revolutionize the diagnosis and treatment of skin diseases by providing personalized and accurate recommendations to healthcare professionals and patients. [ABSTRACT FROM AUTHOR]