Paper Title
SKIN CANCER DIAGNOSIS-LESION SEGMENTATION-REVIEW STUDY
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Registration ID: IJNRD_219317
Published ID: IJNRD2404778
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Keywords
1. Skin lesion segmentation 2. Dermoscopic image analysis 3. Melanoma detection 4. ISIC 2017 dataset 5. Deep learning 6. Convolutional neural networks (CNNs) 7. Image processing 8. Segmentation algorithms 9. Performance evaluation 10. Similarity metrics
Abstract
Skin lesion segmentation plays a crucial role in computer-aided diagnosis of melanoma-most invasive Skin cancer with highest risk of Death. This paper presents a comprehensive review and comparative analysis of state-of-the-art skin lesion segmentation methods using the ISIC 2017 dataset. The dataset, consisting of dermoscopic images of skin lesions, poses unique challenges due to variations in lesion size, shape, and texture. Various deep learning and traditional image processing techniques are explored and evaluated for their effectiveness in segmenting skin lesions accurately for the early detection of Skin cancer which helps in early diagnosis. Performance metrics such as Dice coefficient, Jaccard index, Accuracy, Sensitivity, Specificity are employed to quantify the similarity between the segmented regions and ground truth annotations. The results provide valuable insights into the effectiveness of different segmentation methods and their suitability for use in clinical practice. The results highlight the strengths and limitations of different approaches, providing insights for future research in this domain.
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How To Cite (APA)
GEETHANJALI B N (April-2024). SKIN CANCER DIAGNOSIS-LESION SEGMENTATION-REVIEW STUDY. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h734-h737. https://ijnrd.org/papers/IJNRD2404778.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : h734-h737
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Paper Reg. ID: IJNRD_219317
Published Paper Id: IJNRD2404778
Research Area: Computer Science & TechnologyÂ
Author Type: Indian Author
Country: HASSAN, KARNATAKA, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404778.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404778
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