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.
