Hybrid SqueezeNet-LSTM framework with advanced SegNet segmentation for automated skin disease detection

Abstract

Skin diseases such as pyoderma, scabies, and fungal infections remain a pressing public health concern in India due to poor hygiene, overcrowding, and limited access to care. To address these challenges, this study introduces a novel deep learning framework, the SqueezeNet-Modified Long-Short-Term Memory model, for the automated detection of skin diseases. The system incorporates four core phases: preprocessing via Gaussian filtering to reduce image noise, segmentation using a Modified SegNet enhanced with a Beta-softmax activation for precise lesion isolation, hybrid feature extraction combining shape, texture, colour, d Local Gradient Triangular Patter, and deep features, and robust classification through the SqueezeNet-Modified Long Short-Term Memory model integrated with Multi-Region Window pooling pooling and Focal-log-cosh loss. The innovative Beta-softmax function and Multi-Region Window pooling strategies enhance feature prioritization and classification accuracy. Evaluation in two data sets, one with 1,414 images of vitiligo and psoriasis, and another with 61 samples in four skin conditions, demonstrates superior performance (accuracy: 0.958, sensitivity: 0.953, specificity: 0.948, F-measure: 0.950) over baseline models such as long short-term memory and novel segmented neural networks. This framework provides a scalable solution for dermatological diagnostics in low-resource settings, with future enhancements targeting the expansion to transformer-based approaches and larger clinical data sets.

Keywords

• Skin diseases
• deep learning
• SqueezeNet-Modified LSTM
• automated diagnosis
• image segmentation

References

1. [1] M. Heenaye-Mamode Khan, N. Gooda Sahib-Kaudeer, M. Dayalen, F. Mahomedaly, G.R. Sinha, K.K. Nagwanshi, A. Taylor and others, Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN), Comput. Intell. Neurosci. 1797471, 2022.
2. [2] J. Bu, Y. Lin, L. Q. Qing, G. Hu, P. Jiang, H. F. Hu and E. X. Shen, Prediction of Skin Disease Using a New Cytological Taxonomy Based on Cytology and Pathology with Deep Residual Learning Method, Sci. Rep. 11(1), 13764, 2021.
3. [3] K. Sreekala, N. Rajkumar, R. Sugumar, K.V. Sagar, R. Shobarani, K.P. Krishnamoorthy, A.K. Saini, H. Palivela and A. Yeshitla, Skin Diseases Classification Using Hybrid AI Based Localization Approach, Comput. Intell. Neurosci. 6138490, 2022.
4. [4] M.Q. Hatem, Skin Lesion Classification System Using a K-Nearest Neighbor Algorithm, Vis. Comput. Ind. Biomed. Art 5(1), 1–10, 2022.
5. [5] S.E. Sorour, A.A. Hany, M.S. Elredeny, A. Sedik and R.M. Hussien, An Automatic Dermatology Detection System Based on Deep Learning and Computer Vision, IEEE Access 11 137769–137778, 2023.
6. [6] M.N. Bajwa, K. Muta, M.I. Malik, S.A. Siddiqui, S.A. Braun, B. Homey, A. Dengel and S. Ahmed, Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks, Appl. Sci. 10(7), 2488, 2020.
7. [7] P.N. Srinivasu, J.G. SivaSai, M.F. Ijaz, A.K. Bhoi, W. Kim and J.J. Kang, Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM, Sensors 21(8), 2852, 2021.
8. [8] R. Sadik, A. Majumder, A.A. Biswas, B. Ahammad and M.M. Rahman, An In-depth Analysis of Convolutional Neural Network Architectures with Transfer Learning for Skin Disease Diagnosis, Healthc. Anal. 3, 100143, 2023.

9. [9] G. Cai, Y. Zhu, Y. Wu, X. Jiang, J. Ye and D. Yang, A Multimodal Transformer to Fuse Images and Metadata for Skin Disease Classification, Vis. Comput. 39(7), 2781–2793, 2023.
10. [10] M. Wei, Q. Wu, H. Ji, J. Wang, T. Lyu, J. Liu and L. Zhao, A Skin Disease Classification Model Based on DenseNet and ConvNeXt Fusion, Electronics 12(2), 438, 2023.
11. [11] T.D. Nigat, T.M. Sitote and B.M. Gedefaw, Fungal Skin Disease Classification Using the Convolutional Neural Network, J. Healthc. Eng. 6370416, 2023.
12. [12] S. B. Verma, S. Panda, P. Nenoff, A. Singal, S. M. Rudramurthy, S. Uhrlass, A. Das, K. Bisherwal, D. Shaw and R. Vasani, The unprecedented epidemic-like scenario of dermatophytosis in India: I. Epidemiology, risk factors and clinical features , Indian J. Dermatol., Venereology and Leprology, 89 (3), 421430, 2023.
13. [13] A. Jain, A.C.S. Rao, P.K. Jain and A. Abraham, Multi-type Skin Diseases Classification Using OP-DNN Based Feature Extraction Approach, Multimed. Tools Appl., 1–26, 2022.
14. [14] R. Mohakud and R. Dash, Designing a Grey Wolf Optimization Based Hyper- Parameter Optimized Convolutional Neural Network Classifier for Skin Cancer Detection, J. King Saud Univ. Comput. Inf. Sci. 34(8), 6280–6291, 2022.
15. [15] N. C. F. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler and A. Halpern, Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC), arXiv preprint arXiv:1710.05006, 2017.
16. [16] A. Marka, J. Carter, E. Toto and S. Hassanpour, Automated Detection of Nonmelanoma Skin Cancer Using Digital Images: A Systematic Review, BMC Med. Imaging 19 (1) 21, 2019.
17. [17] S. S. Han, I. J. Moon, W. Lim, I. S. Suh, S. Y. Lee, J. I. Na, S. H. Kim and S. E. Chang, Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network, JAMA Dermatol., 156 (1), 29–37, 2020.
18. [18] O. Zaar, A. Larson, S. Polesie, K. Saleh, M. Tarstedt, A. Olives, A. Suárez, M. Gillstedt and N. Neittaanmäki, Evaluation of the Diagnostic Accuracy of an Online Artificial Intelligence Application for Skin Disease Diagnosis, Acta Derm.-Venereol. 100, 2020.
19. [19] H.Q. Yu and S. Reiff-Marganiec, Targeted Ensemble Machine Classification Approach for Supporting IoT Enabled Skin Disease Detection, IEEE Access 9, 50244–50252, 2021.
20. [20] J.C. Mathew, V. Asha, B. Saju, N. Tressa, P. Imagoudanavar and S.M. Mandave, Detection of Skin Cancer Using Optimized Hybrid Deep Learning Model, in Proc. 2024 Int. Conf. Adv. Electr., Comput., Commun. Sustain. Technol. (ICAECT), 2024.
21. [21] V. Pandurangan, S. Sarojam, P. Narayanan and M. Velayutham, Hybrid Deep Learning-Based Skin Cancer Classification with RPO-SegNet for Skin Lesion Segmentation, Network, 1–28, 2024.
22. [22] S. Armaan, E. Gündogan and M. Kaya, Classification of Skin Lesions Using Squeeze and Excitation Attention Based Hybrid Model of DenseNet and EfficientNet, in Proc. 2024 Int. Conf. Decision Aid Sci. Appl. (DASA), 2024.
23. [23] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit and N. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, CoRR abs/2010.11929, 2020.
24. [24] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y.Wei, Z. Zhang, S. Lin and B. Guo, Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows, in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 10012–10022, Montreal, 2021.
25. [25] A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.-Y. Lo, P. Dollár and R. Girshick, Segment Anything, in Proc. 2023 IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 3992–4003, 2023.
26. [26] V. Balaji, S. Suganthi, R. Rajadevi, V.K. Kumar, B.S. Balaji and S. Pandiyan, Skin Disease Detection and Segmentation Using Dynamic Graph Cut Algorithm and Classification Through Naive Bayes Classifier, Measurement 163, 107922, 2020.
27. [27] H.M. Son, W. Jeon, J. Kim, C.Y. Heo, H.J. Yoon, J. U. Park and T. M. Chung, AI-Based Localization and Classification of Skin Disease with Erythema, Sci. Rep. 11(1), 5350, 2021.
28. [28] C. Dayananda, J. Y. Choi and B. Lee, A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation, IEEE Access 10, 52804–52817, 2022. doi:10.1109/ACCESS.2022.3175188
29. [29] M.D. Alahmadi, Multiscale Attention U-Net for Skin Lesion Segmentation, IEEE Access 10, 59145–59154, 2022.
30. [30] S. M. Thwin and H. S. Park, Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models, Eng. 5(4), 2024.
31. [31] T. Kamalam, Y. Srikanth and P.S. Venkatesh, Skin Lesion Segmentation Detection Using U-Net Architecture, in Proc. 2024 Int. Conf. Wireless Commun. Signal Process. Netw. (WiSPNET), 2024.
32. [32] I. Iyyakutty and G.S. Kumar, vSegNet - A Variant SegNet for Improving Segmentation Accuracy in Medical Images with Class Imbalance and Limited Data, Medinformatics 2(1), 36–48, 2025.
33. [33] J. Liao, T. Zhang, C. Li and Z. Huang, LS-Net: Lightweight Segmentation Network for Dermatological Epidermal Segmentation in Optical Coherence Tomography Imaging, Biomed. Opt. Express 15, 5723–5738, 2024.
34. [34] A. Jlassi, K. ElBedoui and W. Barhoumi, Brain Tumor Segmentation of Lower-Grade Glioma Across MRI Images Using Hybrid Convolutional Neural Networks, ICAART 15, 454–465, 2023.
35. [35] M.D. Kumar, Cardiac Segmentation from MRI Images Using Recurrent & Residual Convolutional Neural Network Based on SegNet and Level Set Methods, Turk. J. Comput. Math. Educ. 12, 1260–1266, 2021.
36. [36] M.R., P.G. Rachana and S.B. J, Segmentation of Tumour from Mammogram Images Using U-SegNet: A Hybrid Approach, Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 11, 387–398, 2022.
37. [37] I. Bratchenko, L. Bratchenko, A. Moryatov, Y. Khristoforova, D. Artemyev, O. Myakinin, A. Orlov, S. Kozlov and V. Zakharov, In Vivo Diagnosis of Skin Cancer with a Portable Raman Spectroscopic Device, Exp. Dermatol. 30, 2021.
38. [38] S. Annepu, S. Saidhu, J. Vurla, R. Kare, S. Koyye and P.S. Kumar, A Review on the Various Methods for Classifying Skin Cancer, in Proc. 2024 Int. Conf. Social Sustain. Innov. Technol. Eng. (SASI-ITE), 284–289, 2024.
39. [39] A.K. Verma and S. Pal, Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method, Appl. Biochem. Biotechnol. 191(2), 637–656, 2020.
40. [40] K. Kalaivani and Y. Asnath Victy Phamila, Modified Wiener Filter for Restoring Landsat Images in Remote Sensing Applications, Pertanika J. Sci. Technol. 26(3), 2018.
41. [41] V. Badrinarayanan, A. Kendall and R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495, 2017.
42. [42] C. Nwankpa, W. Ijomah, A. Gachagan and S. Marshall, Activation Functions: Comparison of Trends in Practice and Research for Deep Learning, arXiv preprint arXiv:1811.03378, 2018.
43. [43] J. Liu and Y. Shi, Image Feature Extraction Method Based on Shape Characteristics and Its Application in Medical Image Analysis, in Proc. ICAIC, 172–178, 2011.
44. [44] A. Hafiane, K. Palaniappan and G. Seetharaman, Joint Adaptive Median Binary Patterns for Texture Classification, Pattern Recognit. 48(8), 2609–2620, 2015.
45. [45] K. Takemura and H. Murakami, Probability Weighting Functions Derived from Hyperbolic Time Discounting: Psychophysical Models and Their Individual Level Testing, Front. Psychol. 7, 778, 2016.
46. [46] D. Srivastava, R. Wadhvani and M. Gyanchandani, A Review: Color Feature Extraction Methods for Content Based Image Retrieval, Int. J. Comput. Eng. Manag. 18(3), 9–13, 2015.
47. [47] W.X. Cheng, P.N. Suganthan and R. Katuwal, Time Series Classification Using Diversified Ensemble Deep Random Vector Functional Link and ResNet Features, Appl. Soft Comput. 112, 107826, 2021.
48. [48] M. Fatima, M.A. Khan, M. Sharif, M. Alhaisoni, A. Alqahtani, U. Tariqe, Y.J. Kim and B. Chang, Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification, Comput. Intell. Neurosci. 2022, 2022.
49. [49] Y. Yao and Z. Huang, Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation, in Proc. ICONIP, 345–353, 2016.
50. [50] N. Beheshti and S. Johnsson, Squeeze U-Net: A Memory and Energy Efficient Image Segmentation Network, in Proc. 2020 IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), 1495–1504, 2020.