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CSG2341 Intelligent Systems

Repository of a CSG2341 Intelligent Systems group project by Mark Jamsek, Joshua Soto-Kitcher, and Jason Knox.

Group Set 2 Team 10 Proposal Report

Machine learning in dermoscopic diagnostics of malignant cutaneous neoplasms
Mark Jamsek, Joshua Soto-Kitcher, and Jason Knox

Abstract

Skin cancer is a fatal disease that impacts one million Australians every year.
Due to the inherent difficulty of accurately diagnosing skin lesions, and the critical
importance of early treatment, the purpose of this report is to review the literature
on machine learning enhanced diagnostics of cutaneous neoplasms. Initial research
found that convolutional neural networks produce the most accurate image classification
models, which led to further investigation into the binary classification models
developed with the AlexNet, ResNet, and VGGNet architectures that were employed in
various skin cancer detection projects. Further, with the problem of distinguishing
not only benign from malignant naevi but also melanocytic and non-melanocytic skin
cancers, additional research into the multiclass classification model implemented
with EfficientNet was also performed. Subsequent findings reveal that SoftMax and
Support Vector Machine (SVM) image classification functions were favoured, and
consistently produced models testing above 90% accuracy. Interestingly, these
results were irrespective of the dataset size. In addition, an on-device inference
application was reviewed, which highlighted both the challenges and prospects of
bringing this technology to mobile devices. As a final point, we propose a detailed
plan to design and develop a VGGNet convolutional neural network to classify images
of skin lesions from the PH2 and/or HAM10000 datasets using a SoftMax and/or
SVM classifier; with the secondary objective of researching the viability of adapting
this technology to mobile devices.

The full paper can be read here.

References

Journal Articles

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Conference Papers

  1. Chakraborty, S., Mali, K., Chatterjee, S., Anand, S., Basu, A., Banerjee, S., Das, M., & Bhattacharya, A. (2017). Image based Skin Disease Detection using Hybrid Neural Network coupled Bag-of-Features. In 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (pp. 242–246). IEEE. https://doi.org/10.1109/UEMCON.2017.8249038

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  2. Ge, Z., Demyanov, S., Chakravorty, R., Bowling, A., & Garnavi, R. (2017). Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, & S. Duchesne (Eds.), Medical Image Computing and Computer Assisted Intervention — MICCAI 2017. Proceedings of the 20th International Conference (pp. 250-258). Springer. https://doi.org/10.1007/978-3-319-66179-7_29

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  3. He, X., Wang, S., Shi, S., Tang, Z., Wang, Y., Zhao, Z., Dai, J., Ni, R., Zhang X., Liu, X., Wu, Z., Yu, W., & Chu, X. (2019). Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. In 2019 IEEE International Conference on Big Data (pp. 4839–4844). IEEE. https://doi.org/10.1109/BigData47090.2019.9006528

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  6. Yan, Y., Kawahara, J., & Hamarneh, G. (2019). Melanoma Recognition via Visual Attention. In A. C. S. Chung, J. C. Gee, P. A. Yushkevich, & S. Bao (Eds.), Information Processing in Medical Imaging: 26th International Conference, IPMI 2019 (pp. 793-804). Springer. https://doi.org/10.1007/978-3-030-20351-1

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