Multi Disease Prediction using Deep Learning

In this paper, we have introduced a MDPDL system which uses a fusion of deep learning approaches such as CNN for feature extraction, Genetic Algorithm for feature optimization, Bi-LSTM for context modeling, and attention mechanism for enhancing the output. This system performs significantly better in terms of diagnostic accuracy, achieving an accuracy of nearly 96%. In comparison to traditional machine learning approaches, our proposed MDPDL framework offers improved spatial representation learning, avoids redundant feature learning, enhances context modeling, and provides more accurate classification results.

[1]  G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, C. I. Sánchez, et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, vol. 42, pp. 60– [3]  A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun, “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks,” Nature, vol. 542, pp. 115–118, 2017. [4]  V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, D. R. Webster, et al., “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA, vol. 316, no. 22, pp. 2402–2410, 2016. [5]  X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. M. Summers, “ChestX-ray14: Large-Scale Chest X-Ray Database and Benchmarks for Multi-Label Classification,” arXiv preprint arXiv:1705.02315, 2017. [6]  N. C. Codella, V. Rotemberg, P. Tschandl, M. E. Celebi, S. Dusza, D. Gutman, H. Kittler, “Skin Lesion Analysis Toward Melanoma Detection: The ISIC 2018 Challenge,” arXiv preprint arXiv:1902.03368, 2019. 88, 2017. [2]    P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, A. Y. Ng, et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv preprint arXiv:1711.05225, 2017. [7]  P. Rajpurkar, J. Irvin, R. L. Ball, K. Zhu, B. Yang, H. Mehta, M. P. Lungren, “MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs,” arXiv preprint arXiv:1712.06957, 2017. [8]  H. Q. Nguyen, T. Q. Pham, Q. V. Nguyen, H. T. Nguyen, M. Q. Lam, L. T. Le, H. B. Nguyen, “VinDr-CXR: An Open Dataset of Chest X-Rays with Radiologist Annotations,” Scientific Data, vol. 9, no. 1, 2022. [9]    O. Ronneberger, P. Fischer, T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Proc. Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241, 2015. [10]   G. Huang, Z. Liu, L. van der Maaten, K. Q. Weinberger, “Densely Connected Convolutional Networks (DenseNet),” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708, 2017.