Skin Cancer Detection using Hybrid Neural Network Model

Abstract

Skin cancer is a prevalent and potentially life-threatening disease, emphasizing the need for early detection to improve patient outcomes. Recent advancements in artificial intelligence and deep learning have given rise to the integration of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, offering a promising approach for enhanced skin cancer detection. This abstract provides an overview of the hybrid CNN-LSTM model, highlighting its potential advantages, challenges, and future prospects in the field. The research focuses on the synergistic combination of CNNs, proficient in image feature extraction, and LSTMs, adept at processing sequential data. This hybrid model effectively analyzes dermatoscopic images and patient histories, offering a holistic approach to skin cancer diagnosis. High-quality, diverse datasets containing dermatoscopic images and patient records play a pivotal role in training and validating the CNN-LSTM model. These datasets are crucial for model development, addressing class imbalances, and capturing rare skin cancer types. Transfer learning is explored, enabling the fine-tuning of pre-trained CNN models for dermatoscopic image analysis, accelerating model training and leveraging existing knowledge. The LSTM component processes sequential data, allowing the model to integrate temporal information into its decision-making process. The abstract acknowledges challenges related to interpretability, data privacy, and model robustness, emphasizing the need for transparent and ethical handling of sensitive medical records, particularly in a medical context. Furthermore, the importance of clinical validation and regulatory compliance is highlighted to ensure the effectiveness and safety of CNN-LSTM models in real-world healthcare settings.