Abstract
Automated analysis of dermoscopic images for detecting malignant lesions can improve diagnostic performance and reduce premature deaths. While several automated classification algorithms using deep convolutional neural network (DCNN) models have been proposed, the need for performance improvement remains. The key limitations of developing a robust DCNN model for the dermoscopic image classification are (a) sub-sampling or pooling layer in traditional DCNN has theoretical drawbacks in capturing object-part relationship, (b) increasing the network depth can improve the performance but is prone to suffer from the vanishing gradient problem, and (c) due to imbalanced dataset, the trained DCNN tends to be biased towards the majority classes. To overcome these limitations, we propose a novel deep Attention Residual Capsule Network (ARCN) for dermoscopic image classification to diagnose skin diseases. The proposed model combines the concept of residual learning, self-attention mechanism, and capsule network. The residual learning is employed to address the vanishing gradient problem, the self-attention mechanism is employed to prioritize important features without using any extra learnable parameters, capsule network is employed to cope up with information loss due to the sub-sampling (max-pooling) layer. To deal with the classifier’s bias toward the majority classes, a novel Mini-Batch-wise weight-balancing Focal Loss strategy is proposed. HAM10000, a benchmark dataset of dermoscopic images is used to train the deep model and evaluate the performance. The ARCN-18 (modification of ResNet-18) network trained with the proposed loss produces an accuracy of 0.8206 for the considered test set.
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References
Arnold, M., et al.: Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. JAMA Dermatol. 158(5), 495–503 (2022). https://doi.org/10.1001/jamadermatol.2022.0160
Barata, C., Celebi, M.E., Marques, J.S.: Improving dermoscopy image classification using color constancy. IEEE J. Biomed. Health Inform. 19(3), 1146–1152 (2015)
Chollet, F., et al.: Keras (2015). https://keras.io
Esteva, A., et al.: Corrigendum: dermatologist-level classification of skin cancer with deep neural networks. Nature 546, 686–686 (2017)
Ge, Z., Demyanov, S., Chakravorty, R., Bowling, A., Garnavi, R.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 250–258. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_29
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS (2010)
Hassanin, M., Anwar, S., Radwan, I., Khan, F.S., Mian, A.: Visual attention methods in deep learning: an in-depth survey. arXiv preprint arXiv:2204.07756 (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2015)
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. arXiv abs/1506.02025 (2015)
Kittler, H., H., P., K., W., M., B.: Diagnostic accuracy of dermoscopy. Lancet Oncol. 3(3), 159–165 (2002)
Lafraxo, S., Ansari, M.E., Charfi, S.: Melanet: an effective deep learning framework for melanoma detection using dermoscopic images. Multimedia Tools Appl. 81(11), 16021–16045 (2022)
Li, H., Zeng, N., Wu, P., Clawson, K.: Cov-net: a computer-aided diagnosis method for recognizing COVID-19 from chest x-ray images via machine vision. Expert Syst. Appl. 118029 (2022)
Lin, T.Y., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007 (2017)
Pal, A., Chaturvedi, A., Garain, U., Chandra, A., Chatterjee, R.: Severity grading of psoriatic plaques using deep CNN based multi-task learning. In: 23rd International Conference on Pattern Recognition (ICPR 2016), December 2016
Pal, A., et al.: Micaps: multi-instance capsule network for machine inspection of Munro’s microabscess. Comput. Biol. Med. 140, 105071 (2022)
Rajasegaran, J., Jayasundara, V., Jayasekara, S., Jayasekara, H., Seneviratne, S., Rodrigo, R.: DeepCaps: going deeper with capsule networks. arXiv abs/1904.09546 (2019)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. arXiv abs/1710.09829 (2017)
Salma, W., Eltrass, A.S.: Automated deep learning approach for classification of malignant melanoma and benign skin lesions. Multimedia Tools Appl. 1–18 (2022)
Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5 (2018). https://doi.org/10.1038/sdata.2018.161
Vestergaard, M.E., Macaskill, P., Holt, P.E., Menzies, S.W.: Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting. Br. J. Dermatol. 159, 669–676 (2008)
Xi, E., Bing, S., Jin, Y.: Capsule network performance on complex data. arXiv e-prints arXiv:1712.03480, December 2017
Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 994–1004 (2017)
Zhang, J., Xie, Y., Xia, Y., Shen, C.: Attention residual learning for skin lesion classification. IEEE Trans. Med. Imaging, 1 (2019)
Acknowledgment
Dr. Pal’s work is partially supported by the intramural research program of the National Library of Medicine and the National Institutes of Health, USA. Dr. Antani’s work is supported by the intramural research program of the National Library of Medicine and the National Institutes of Health, USA. Dr. Garain’s work is supported by Science and Engineering Research Board (SERB), Dept. of Science and Technology (DST), Govt. of India through Grant File No. SPR/2020/000495.
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Pal, A., Ray, S., Antani, S., Garain, U. (2023). Attention Residual Capsule Network for Dermoscopy Image Classification. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_9
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