Tackling the class imbalanced dermoscopic image classification using data augmentation and GAN

Divya G, Liang Q, Wang S, Zhu X (2021) An Empirical Study of Deep Learning Frameworks for Melanoma Cancer Detection using Transfer Learning and Data Augmentation. In 2021 IEEE International Conference on Big Knowledge (ICBK), pp. 38–45. IEEE Ali K, Shaikh ZA, Khan AA, Laghari AA. Multiclass skin cancer classification using efficientNets–a first step towards preventing skin cancer. Neurosci Inf 2022;2(4):100034 Devansh B, Choromanska A, Berman RS, Stein JA, Polsky D (2019) Towards automated melanoma detection with deep learning: Data purification and augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0 Diallo, Papa Abdou Karim Karou, and Yun Ju. ”Accurate detection of covid-19 using k-efficientnet deep learning image classifier and k-covid chest x-ray images dataset.” In 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 1527–1531. IEEE, 2020. Vasconcelos CN, Nader Vasconcelos B (2017) Convolutional neural network committees for melanoma classification with classical and expert knowledge based image transforms data augmentation.” arXiv preprint arXiv:1702.07025 Salekshahrezaee Z, Leevy JL, Khoshgoftaar TM (2021) Feature extraction for class imbalance using a convolutional autoencoder and data sampling. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pp. 217–223. IEEE Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier gans. International conference on machine learning. 2017(pp. 2642-2651). PML Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 Ze L, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll´ar P, Zitnick CL (2014) ”Microsoft coco: Common objects in context.” In European conference on computer vision, pp. 740–755. Springer, Cham Atila U, Uçar M, Akyol K, Uçar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Ecol Inform 61:101182 He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5(1):1–9 Jan MT, Hashemi A, Jang J, Yang K, Zhai J, Newman D, Tappen R, Furht B (2023) Non-intrusive Drowsiness Detection Techniques and Their Application in Detecting Early Dementia in Older Drivers. In Proceedings of the Future Technologies Conference, pp. 776–796. Springer, Cham Alsaidi M, Altaher AS, Tanveer Jan M, Altaher A, Salekshahrezaee Z (2022) COVID-19 Classification Using Deep Learning Two-Stage Approach. arXiv preprint arXiv:2211.15817 Xiang A, Wang F (2019) Towards interpretable skin lesion classification with deep learning models. In AMIA annual symposium proceedings, vol. 2019, p. 1246. American Medical Informatics Association Chaturvedi SS, Tembhurne JV, Diwan T (2020) A multi-class skin Cancer classification using deep convolutional neural networks. Multimed Tools Appl 79(39-40):28477–28498 Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25–32 Nyíri T, Kiss A (2018) Novel ensembling methods for dermatological image classification. In International conference on theory and practice of natural computing, pp. 438–448. Springer, Cham Shahin AH, Kamal A, Elattar MA (2018) Deep ensemble learning for skin lesion classification from dermoscopic images. In 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), pp. 150–153. IEEE Menegola A, Fornaciali M, Pires R, Bittencourt FV, Avila S, Valle E. Knowledge transfer for melanoma screening with deep learning. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017) 2017 Apr 18 (pp. 297–300). IEEE Pomponiu V, Nejati H, Cheung N-M (2016) Deepmole: Deep neural networks for skin mole lesion classification. In 2016 IEEE international conference on image processing (ICIP), pp. 2623–2627. IEEE Milton Md AA (2019) Automated skin lesion classification using ensemble of deep neural networks in ISIC 2018: Skin lesion analysis towards melanoma detection challenge. arXiv preprint arXiv:1901.10802 Hasan HA, Ibrahim AA (2020) Hybrid Detection Techniques for Skin Cancer Images. In 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–8. IEEE Zhou B, Zhao H, Puig X, Xiao T, Fidler S, Barriuso A, Torralba A (2019) Semantic understanding of scenes through the ade20k dataset. Int J Comput Vision 127(3):302–321 Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) Doubleu-net: A deep convolutional neural network for medical image segmentation. In 2020 IEEE 33rd International symposium on computer-based medical systems (CBMS), pp. 558–564. IEEE Gessert N, Nielsen M, Shaikh M, Werner R, Schlaefer A (2020) Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. Methods 7:100864 Yao P, Shen S, Mengjuan Xu, Liu P, Zhang F, Xing J, Shao P, Kaffenberger B, Ronald XXu (2021) Single model deep learning on imbalanced small datasets for skin lesion classification. IEEE Trans Med Imaging 41(5):1242–1254 Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 Goodfellow I, Pouget-Abadie J, Mirza M, Bing Xu, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144 Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B (2019) Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592 Du M, Liu N, Xia Hu (2019) Techniques for interpretable machine learning. Commun ACM 63(1):68–77 Gajera HK, Nayak DR, Zaveri MA (2023) A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features. Biomed Signal Process Control 79:104186 Alenezi F, Armghan A, Polat K (2023) A multi-stage melanoma recognition framework with deep residual neural network and hyperparameter optimizationbased decision support in dermoscopy images. Expert Syst Appl 215:119352 Emara T, Afify HM, Ismail FH, Hassanien AE (2019) A modified inception-v4 for imbalanced skin cancer classification dataset. In 2019 14th International Conference on Computer Engineering and Systems (ICCES), pp. 28–33. IEEE Jan MT, Moshfeghi S, Conniff JW, Jang J, Yang K, Zhai J, Rosselli M, Newman D, Tappen R, Furht B (2023) Methods and Tools for Monitoring Driver’s Behavior. arXiv preprint arXiv:2301.12269 Chen K, Zhuang D, Morris Chang J (2022) SuperCon: Supervised contrastive learning for imbalanced skin lesion classification. arXiv preprint arXiv:2202.05685 Ozturk S, Cukur T (2022) Deep clustering via center-oriented margin free-triplet loss for skin lesion detection in highly imbalanced datasets. IEEE J Biomed Health Inform 26(9):4679–4690 Qian S, Ren K, Zhang W, Ning H (2022) Skin lesion classification using CNNs with grouping of multi-scale attention and class-specific loss weighting. Comput Methods Programs Biomed 226:107166 Shen S, Xu M, Zhang F, Shao P, Liu H, Xu L, Zhang C et al (2022) A low-cost high-performance data augmentation for deep learning-based skin lesion classification. BME Frontiers 2022 Baur, Christoph, Shadi Albarqouni, and Nassir Navab. ”MelanoGANs: high resolution skin lesion synthesis with GANs.” arXiv preprint arXiv:1804.04338 (2018). Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90 Yu Y, Gong Z, Zhong Ping, Shan J (2017) Unsupervised representation learning with deep convolutional neural network for remote sensing images. In Image and Graphics: 9th International Conference, ICIG 2017, Shanghai, China, September 13–15, 2017, Revised Selected Papers, Part II 9, pp. 97–108. Springer International Publishing Gao H, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708 Qin Z, Liu Z, Zhu P, Xue Y (2020) A GAN-based image synthesis method for skin lesion classification. Comput Methods Programs Biomed 195:105568 Mukti, Zahan I, Biswas D (2019)Transfer learning based plant diseases detection using ResNet50. In 2019 4th International conference on electrical information and communication technology (EICT), pp. 1–6. IEEE Suhita Ray (2018) Disease classification within dermascopic images using features extracted by resnet50 and classification through deep forest. arXiv preprint arXiv:1807.05711 Shabbir A, Ali N, Ahmed J, Zafar B, Rasheed A, Sajid M, Ahmed A, Hanif Dar S (2021) Satellite and scene image classification based on transfer learning and fine tuning of ResNet50. Mathematical Problems in Engineering 2021:1–18 Council of the European Union (2016) Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), available at http://data.europa.eu/eli/reg/2016/679/2016-05-04, Accessed 21 February 2023 Selvaraju RR, Cogswell M, Das Ak, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pp. 618–626 Lucieri, A, Bajwa MN, Braun SA, Malik MI, Dengel A, Ahmed S (2020) On interpretability of deep learning based skin lesion classifiers using concept activation vectors. In 2020 international joint conference on neural networks (IJCNN), pp. 1–10. IEEE Ulus C, Wang Z, Iqbal SMA, Khan KMdS, Zhu X (2022) Transfer Na¨ıve Bayes Learning using Augmentation and Stacking for SMS Spam Detection. In 2022 IEEE International Conference on Knowledge Graph (ICKG), pp. 275–282. IEEE Altaher A, Salekshahrezaee Z, Zadeh AA, Rafieipour H, Altaher A (2020) Using multi-inception CNN for face emotion recognition. Journal of Bioengineering Research 3, no. 1: 1–12. Abidalkareem, AJ, Abd MA, Ibrahim AK, Zhuang H, Altaher AS, Muhamed A (2020.) Diabetic retinopathy (DR) severity level classification using multimodel convolutional neural networks. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1404–1407. IEEE Limeros SC, Majchrowska S, Zoubi MK, Ros´en A, Suvilehto J, Sj¨oblom L, Kjellberg M (2022) GAN-based generative modelling for dermatological applications– comparative study. In Arxiv, arXiv:2208.11702 Yosinski J, Clune J, Bengio Y, Lipson H (2014) Howtransferable are features in deep neural networks? In Proc. of NIPS Man Wu, Wang S, Pan S, Terentis AC, Strasswimmer J, Zhu X (2021) Deep learning data augmentation for Raman spectroscopy cancer tissue classification. Sci Rep 11:23842 Brinker TJ, Hekler A, Utikal JS, Grabe N, Schadendorf D, Klode J, Berking C, Steeb T, Enk AH (2018) Christof von Kalle. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review, J Med Int Res 20(10):e11936 Sikkandar Y, Mohamed, Alrasheadi BA, Prakash NB, Hemalakshmi GR, Mohanarathinam A, Shankar K (2021) Deep learning based an automated skin lesion segmentation and intelligent classification model. J Ambient Intell humanized Comput 12:3245-3255. Mirikharaji Z, Abhishek K, Bissoto A, Barata C, Avila S, Valle E, Celebi ME, Hamarneh G (2023) A survey on deep learning for skin lesion segmentation. Med Image Anal: 102863 Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and ComputerAssisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp. 234–241. Springer International Publishing Pollastri F, Bolelli F, Paredes R, Grana C (2020) Augmenting data with GANs to segment melanoma skin lesions. Multimedia Tools and Applications 79:15575–15592 Hasan MdK, Dahal L, Samarakoon PN, Tushar FI, Mart´I R ((2020)) DSNet: Automatic dermoscopic skin lesion segmentation. Computers in biology and medicine 120: 103738 Canalini L, Pollastri F, Bolelli F, Cancilla M, Allegretti S, Grana C (2019) Skin lesion segmentation ensemble with diverse training strategies. In Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part I 18, pp. 89–101. Springer International Publishing Seifallahi M, HasaniMehraban A, Galvin JE, Ghoraani B (2022) Alzheimer’s disease detection using comprehensive analysis of Timed Up and Go test via Kinect V. 2 camera and machine learning. IEEE Trans Neural Syst Rehabil Eng 30:1589–1600 Soudani A, Barhoumi W (2019) An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction. Expert Syst Appl 118:400–410 Xie Y, Zhang J, Xia Y, Shen C (2020) A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans Med Imaging 39(7):2482–2493 Jin Q, Cui H, Sun C, Meng Z, Ran Su (2021) Cascade knowledge diffusion network for skin lesion diagnosis and segmentation. Appl Soft Comput 99:106881 Lei B, Xia Z, Jiang F, Jiang X, Ge Z, Yanwu Xu, Qin J, Chen S, Wang T, Wang S (2020) Skin lesion segmentation via generative adversarial networks with dual discriminators. Med Image Anal 64:101716 Tu W, Liu X, Wei Hu, Pan Z (2019) Dense-residual network with adversarial learning for skin lesion segmentation. IEEE Access 7:77037–77051 Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille L, Zhou Y (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 Gulzar Y, Khan SA (2022) Skin lesion segmentation based on vision transformers and convolutional neural networks—A comparative study. Appl Sci 12(12):5990 Iqbal SMA, Mahgoub I, Du E, Leavitt MA, Asghar W (2022) Development of a wearable belt with integrated sensors for measuring multiple physiological parameters related to heart failure. Sci Rep 12(1):20264 Iqbal SMA, Asghar W (2023) Smartphone Integration with Point-of-Care Devices for Disease Diagnostics. In Emerging Technologies In Biophysical Sciences: A World Scientific Reference: Volume 3: Emerging Technologies for Diagnostics, pp. 317–335 Jasil SPG, Ulagamuthalvi V (2021) Deep learning architecture using transfer learning for classification of skin lesions. J Ambient Intell Humanized Comput: 1–8 Khan Attique, Muhammad Muhammad Sharif, Akram Tallha, Kadry Seifedine, Hsu Ching-Hsien (2022) A two-stream deep neural network-based intelligent system for complex skin cancer types classification. Int J Intell Syst 37(12):10621–10649 Naqvi Maryam, Gilani Syed Qasim, Syed Tehreem, Marques Oge, Kim Hee-Cheol (2023) Skin Cancer Detection Using Deep Learning-A Review. Diagnostics (Basel, Switzerland) 13(11):1911 Qasim Gilani S, Syed T, Umair M, Marques O. Skin Cancer Classification Using Deep Spiking Neural Network. J Digit Imaging (2023): 1–11 Gilani SQ, Marques O (2023) Skin lesion analysis using generative adversarial networks: A review. Multimedia Tools Appl: 1–42