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The approach jointly improves learning rate, batch size, and training epochs via global and localized search, targeting both strong performance and economical resource use. Results report 98.48% accuracy, 97.67% precision, 100% recall, and an ROC-AUC of 99.79%, while Grad-CAM heatmaps and superimposed images provide interpretability for malignant lesion detection and support earlier, more reliable clinical diagnosis.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/advanced-skin-cancer-prediction-with-medical-image-data-using-mobilenetv2-deep-learning-and-optimized-techniques/373601/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/advanced-skin-cancer-prediction-with-medical-image-data-using-mobilenetv2-deep-learning-and-optimized-techniques/373601.png","ImageObject",300,407,{"name":92,"@type":93},"Stanford","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early skin cancer diagnosis important in this study?","Question",{"text":112,"@type":113},"Successful treatment decreases sharply when melanoma is not handled in early stages, because it can spread aggressively. 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The chances of successful treatment are greatly reduced if the melanoma is not treated in its early stages because it could spread aggressively. Hence, the diagnosis of skin cancer is very challenging as skin lesions are highly subjective to analyze and that type of expertise is exceedingly specialized. While there is an increase in the prevalence of skin cancer across the globe, there is an increase need of automated diagnostic systems that could aid medical personnel in making appropriate decisions within the requisite timelines. This study proposes construction of a deep learning model built on the MobileNetV2 architecture that has been memetic optimized for hyperparameter tuning. The memetic algorithm employs both global and localized search techniques to fine-tune the model parameters that include learning rate, batch size, and number of epochs to boost the efficacy of the model. This makes it possible for the proposed model to achieve high performance while remaining economical on resources. This makes the model suitable for real world clinical settings. The model achieved exceptional results, with 98.48% accuracy, 97.67% precision, and 100% recall, highlighting its strong ability to detect malignant lesions. The ROCAUC score of 99.79% further demonstrates its outstanding capability to differentiate between benign and malignant lesions. Notably, visualizations such as the Grad-CAM heatmap and Superimposed Image were crucial in providing interpretability to the model’s decision-making process. The Grad-CAM heatmap highlighted the regions of interest in the lesions, showing how the model focused on key structural features. The Superimposed Image combined these heatmaps with the original lesion images, making it clear which parts of the lesions influenced the model’s classification. These results underscore the potential of deep learning models, optimized with the memetic algorithm, to significantly improve skin cancer detection. By offering both high accuracy and interpretability, this model presents a valuable tool for dermatologists, facilitating faster and more reliable early diagnosis and ultimately improving patient outcomes.  \nKeywords Skin cancer, Deep learning, Memetic algorithm, MobileNetv2, Image prediction  \nSkin cancer is one of the most common and deadly diseases in the world, especially melanoma. It originates from skin cells’ uncontrolled multiplication, primarily caused by ultraviolet (UV) radiation. If not detected at the right stage, it could cause metastasis1. The higher the stage of cancer at time of diagnosis, the lower the chances of survival. Consequently, it is crucial that skin cancer be detected early2. While there have been considerable developments in the field of dermatology, skin lesion analysis continues to be a significant barrier owing to its subjectivity and the level of skill involved3.  \nMachine learning and deep learning have transformed nearly every area of medical image analysis by improving the accuracy of diagnoses and prognosis4. Detecting malignant formations in medical images is an example. With the advent of digital pathology and radiology, the ability to accurately detect both benign and malignant lesions is crucial for early diagnosis, as well as devising an appropriate treatment modality5. In the last few years, deep learning methods, especially Deep Convolutional Neural Networks (CNN), have excelled in many areas of image classification, including medical imaging6.  \nWith the use of a MobileNetV2 architecture memetic algorithm for hyperparameter optimization, this study presents a new paradigm for the classification of skin tumors focusing on t","cbCaiqGb6Rzps1FE","https://ap.wps.com/l/cbCaiqGb6Rzps1FE","pdf",2313769,13,"English","# Materials and methods\n## Dataset construction and preprocessing","[{\"question\":\"Why is early skin cancer diagnosis important in this study?\",\"answer\":\"Successful treatment decreases sharply when melanoma is not handled in early stages, because it can spread aggressively. Early detection is therefore crucial for improving survival and outcomes.\"},{\"question\":\"What model architecture and optimization method are used?\",\"answer\":\"The study builds a deep learning model based on MobileNetV2 and incorporates a memetic algorithm to tune hyperparameters such as learning rate, batch size, and number of epochs.\"},{\"question\":\"How does the study interpret the model’s decisions?\",\"answer\":\"Interpretability is supported through Grad-CAM heatmaps and superimposed images that highlight lesion regions influencing classification between benign and malignant types.\"}]","Advanced skin cancer prediction with medical image data using MobileNetV2 deep learning and optimized techniques | PDF",1790199994,33]