Melanoma Detection and Classification in Dermoscopic Images using resnet50 and Hair removal feature

(1) * Akshaya K P Mail (Dept of Computer Science and Engineering College of Engineering Trivandrum Kerala, India)
(2) Prafulla Phalgunan Mail (Dept of Computer Science and Engineering College of Engineering Trivandrum Kerala, India)
*corresponding author

Abstract


Melanoma is the most common skin cancer, and it is increasing widely. Automatic skin lesion detection from dermoscopic images remains a challenging task. Many efforts have been dedicated to this challenge using various methods, but due to its poor robustness, it is not good for the analysis of melanoma skin lesions. Propose a method for skin lesion detection and classification tasks simultaneously to make sure feature learning is successful. The base of feature pyramid networks and region proposal networks is ResNet50, which is used here. The network learns features more quickly using a three-phase cooperative training technique. Before entering this model, the hairs from the images are removed

Keywords


Skin lesion analysis, end-to-end multi-tasking framework, deep learning, melanoma segmentation, convolu- tional neural networks

   

DOI

https://doi.org/10.29099/ijair.v8i2.1300
      

Article metrics

10.29099/ijair.v8i2.1300 Abstract views : 60 | PDF views : 12

   

Cite

   

Full Text

Download

References


. C. Lu, M. Mahmood, N. Jha and M. Mandal, ”Automated Segmentation

of the Melanocytes in Skin Histopathological Images,” in IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 2, pp. 284-296, March 2013, doi: 10.1109/TITB.2012.2199595

. E. Ahn et al., ”Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images,” in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 6, pp. 1685-1693, Nov. 2017, doi: 10.1109/JBHI.2017.2653179.

. R. Ashraf et al., ”Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection,” in IEEE Access, vol. 8, pp. 147858-147871, 2020, doi: 10.1109/ACCESS.2020.3014701.

. S. Albahli, N. Nida, A. Irtaza, M. H. Yousaf and M. T. Mah- mood, ”Melanoma Lesion Detection and Segmentation Using YOLOv4- DarkNet and Active Contour,” in IEEE Access, vol. 8, pp. 198403- 198414, 2020, doi: 10.1109/ACCESS.2020.3035345.

. A. Mirbeik-Sabzevari, S. Li, E. Garay, H. -T. Nguyen, H. Wang and N. Tavassolian, ”Synthetic Ultra-High-Resolution Millimeter- Wave Imaging for Skin Cancer Detection,” in IEEE Transactions on Biomedical Engineering, vol. 66, no. 1, pp. 61-71, Jan. 2019, doi: 10.1109/TBME.2018.2837102.

. K. Korotkov et al., ”An Improved Skin Lesion Matching Scheme

in Total Body Photography,” in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 586-598, March 2019, doi: 10.1109/JBHI.2018.2855409.

. K. Shimizu, H. Iyatomi, M. E. Celebi, K. -A. Norton and M. Tanaka, ”Four-Class Classification of Skin Lesions With Task Decomposition Strategy,” in IEEE Transactions on Biomedical Engineering, vol. 62, no. 1, pp. 274-283, Jan. 2015, doi: 10.1109/TBME.2014.2348323.

. M. Goyal, A. Oakley, P. Bansal, D. Dancey and M. H. Yap, ”Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods,” in IEEE Access, vol. 8, pp. 4171-4181, 2020, doi: 10.1109/ACCESS.2019.2960504.

. Uddin, Md Kamal Azad, Ibrahim Bhuiyan, AH. (2013). ”Image Processing for Skin Cancer Features Extraction”. International Journal of Scientific Engineering Research. 4.

. L. Song, J. Lin, Z. J. Wang and H. Wang, ”An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis,” in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2912-2921, Oct. 2020, doi: 10.1109/JBHI.2020.2973614




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

________________________________________________________

The International Journal of Artificial Intelligence Research

Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung

Email: jurnal.ijair@gmail.com

View IJAIR Statcounter

Creative Commons License
This work is licensed under  Creative Commons Attribution-ShareAlike 4.0 International License.