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Systematic Literature Review: Advancements in Skin Cancer Diagnosis Using Convolutional Neural Networks and Dermatoscopic Imaging
This study conducts a systematic literature review (SLR) to analyze the application of CNN in automated diagnostic systems for skin cancer using dermatoscopic images. The review examines methods, architectures, and datasets used in recent studies, focusing on their accuracy, efficiency, and limitations. It highlights the adoption of models such as GoogLeNet, ResNet-50, and YOLOv8, which have achieved accuracy levels exceeding 90%, demonstrating the capability of CNNs in distinguishing between benign and malignant lesions. The findings reveal that while CNNs offer high precision and recall, challenges remain in terms of overfitting, dataset diversity, and computational cost. This study underscores the need for larger and more balanced datasets, advanced augmentation techniques, and optimized architectures to enhance model generalizability. The research aims to contribute to the development of robust, efficient, and accessible AI-based diagnostic tools for early skin cancer detection, improving clinical decision-making and patient care.
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systematic literature review advancements in skin cancer diagnosis using convolutional neural networks and dermatoscopic imaging this study conducts a systematic literature review slr to analyze the application of cnn in automated diagnostic systems for skin cancer using dermatoscopic images the review examines methods architectures and datasets used in recent studies focusing on their accuracy efficiency and limitations it highlights the adoption of models such as googlenet resnet 50 and yolov8 which have achieved accuracy levels exceeding 90 demonstrating the capability of cnns in distinguishing between benign and malignant lesions the findings reveal that while cnns offer high precision and recall challenges remain in terms of overfitting dataset diversity and computational cost this study underscores the need for larger and more balanced datasets advanced augmentation techniques and optimized architectures to enhance model generalizability the research aims to contribute to the development of robust efficient and accessible ai based diagnostic tools for early skin cancer detection improving clinical decision making and patient care
Jaro Winkler
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| 2. vvv | 114 words | 0.53 | systematic : 0, literature : 0, review : 0.5, advancements : 0.47, in : 0, skin : 0, cancer : 0, diagnosis : 0, using : 0, convolutional : 0.47, neural : 0, networks : 0, and : 0, dermatoscopic : 0, imaging : 0, this : 0, study : 0, conducts : 0, a : 0, slr : 0, to : 0, analyze : 0, the : 0, application : 0, of : 0, cnn : 0, automated : 0, diagnostic : 0, systems : 0, for : 0, images : 0, examines : 0, methods : 0, architectures : 0, datasets : 0, used : 0, recent : 0, studies : 0, focusing : 0, on : 0, their : 0, accuracy : 0, efficiency : 0, limitations : 0, it : 0, highlights : 0, adoption : 0, models : 0, such : 0, as : 0, googlenet : 0, resnet : 0, 50 : 0, yolov8 : 0.5, which : 0, have : 0.53, achieved : 0.49, levels : 0.5, exceeding : 0, 90 : 0, demonstrating : 0, capability : 0, cnns : 0, distinguishing : 0, between : 0, benign : 0, malignant : 0, lesions : 0, findings : 0, reveal : 0.5, that : 0, while : 0, offer : 0, high : 0, precision : 0, recall : 0, challenges : 0, remain : 0, terms : 0, overfitting : 0.47, dataset : 0, diversity : 0.48, computational : 0, cost : 0, underscores : 0, need : 0, larger : 0, more : 0, balanced : 0, advanced : 0.49, augmentation : 0, techniques : 0, optimized : 0, enhance : 0, model : 0, generalizability : 0, research : 0, aims : 0, contribute : 0, development : 0.47, robust : 0, efficient : 0, accessible : 0, ai : 0, based : 0, tools : 0, early : 0, detection : 0, improving : 0.48, clinical : 0, decision : 0, making : 0, patient : 0, care : 0 |
Average Result
66.39%
0.66388888888889