SKIN CANCER DIAGNOSIS-LESION SEGMENTATION-REVIEW STUDY

Registration ID: IJNRD_219317

Published ID: IJNRD2404778

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GEETHANJALI B N

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

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.

Published Paper   E-Certificate

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

Volume 9 Issue 4, April-2024

Pages : h734-h737

Paper Reg. ID: IJNRD_219317

Published Paper Id: IJNRD2404778

Downloads: 000121995

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: HASSAN, KARNATAKA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404778.pdf

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