Deep Multilevel Feature Fusion: An Xception-Based Framework Enhanced by Assorted Attention Mechanism for Improved Melanoma Diagnosis
DOI:
https://doi.org/10.32628/CSEIT251112401Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Melanoma Diagnosis, Feature ExtractionAbstract
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have a game-changing potential in melanoma diagnosis and treatment. Utilizing these technologies can tremendously increase the accuracy and efficiency of melanoma detection as they rely on algorithms and neural networks to process large volumes of data quickly and accurately like never before. The DMFFX(Deep Multilevel Feature Fused Xception) for feature extraction model, followed by a segmentation model of AAMBCS(Assorted Attention Mechanism based Convolutional Segmentation), shows the contribution of AI in improving image quality and diagnostic accuracy. By employing DEECO (Differential Evolution Based Enhanced Colour Optimization) based preprocessing and the Xception network to enhance the results, the classification and segmentation processes become more potent and efficient, resulting in accurate and reliable results. The study emphasizes the critical role of early detection in enhancing patient outcomes and survival rates. AI-powered technologies present many benefits by offering standard and reliable evaluations that reduce the human element and the opportunity for error. While the developments are promising, researchers in the field of AI in healthcare need to work on overcoming the challenges and research gaps identified in the study to deliver the real-time benefits the technology can deliver to healthcare.
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