Efficiency Analysis of Firefly Optimization-Enhanced GAN-Driven Convolutional Model for Cost-Effective Melanoma Classification

Abstract

Early identification is essential for successful treatment of melanoma, a potentially fatal type of skin cancer. This work takes a fresh approach to addressing the urgent need for an accurate and economical melanoma categorization system. Inaccuracy, efficiency, and resource usage are common problems with current techniques. A model that incorporates a number of innovative methods to get beyond these restrictions was used in this study. To improve data quality, first applied the preprocessing with a Gaussian filter and augment our dataset with Generative Adversarial Networks (GAN). To extract and classify features, this suggested model makes use of Convolutional Long Short-Term Memory (LSTM) networks. The model performs better and is substantially more accurate when Firefly Optimization is used. It analyses the model's ability to lower healthcare costs by doing a cost-effective analysis, especially when detecting melanoma, including situations involving bleeding lesions. The proposed FFO Enhanced Conv-LSTM's cost-effective analysis makes it possible to compare it favourably to deep convolutional neural networks (DCNN), showcasing its promise for melanoma classification accuracy and healthcare resource allocation optimization. For this study, Python software was used as the implementation tool. The suggested model achieves a 99.1% accuracy rate, which is better than current techniques. A comparative study with well-known models such as Res Net 50, Mobile Net, and Dense Net 169 highlights the notable enhancement provided by the proposed Firefly Optimization-enhanced Conv-LSTM method. This model offers a promising advancement in the precise and economical classification of melanoma due to its high accuracy and cost-effectiveness. In comparison to existing approaches like Res Net 50, Mobile Net, and Dense Net 169, the suggested Firefly Optimization-enhanced Convolutional LSTM (FFO Enhanced Conv-LSTM) method shows an average gain of roughly 5.6% in accuracy.