Emperical Analysis of Nail Diseases through Using Hybrid Algorithms of LSTM and CNN

This research presents a novel method that uses CNNs and LSTMs to improve nail-related diagnosis accuracy and efficiency. We investigate the interface of innovative machine learning and medical diagnostics in dermatology, where correct diagnosis is crucial. This research aims to test our CNN-LSTM model on various nail situations. We measured our model’s accuracy to evaluate its performance. The model’s accuracy scores in the table demonstrate its ability to categorize nail conditions, revealing its practical utility. The accuracy for each nail state is shown in the table. Staring at the values shows significant model performance differences across situations. With 94% accuracy, nail fungus is the most accurate ailment. This excellent result shows the model’s Nail Fungus detection skills. Bau’s Lines’ 92% accuracy rate shows the model’s ability to classify this circumstance. Although Hangnails and Ingrown Toenails have less accurate percentages at 93% and 91%, accordingly, the model can still diagnose them. These findings demonstrate the adaptability inherent in our CNN-LSTM approach, which can handle a variety of nail-related problems with diverse properties. Improvements to medical diagnoses through machine learning are not isolated. It affects healthcare image analysis as well as deep learning. CNN and LSTM topologies could transform healthcare, especially skincare, where accurate and fast diagnosis is crucial. Precision requires ongoing research and improvement. To improve model accuracy and robustness, the paper emphasizes class imbalances, model parameter refinement, and dataset expansion. Overall, this study advances the integration of modern machine-learning approaches with dermatological diagnosis. The accuracy values prove the model’s nail condition categorization accuracy. This discovery is a milestone and lays the groundwork for medical image analysis, healthcare diagnostics, and dermatology accuracy.