Unveiling the Complexity of Medical Imaging through Deep Learning Approaches

Year 2023, Volume: 5 Issue: 4, 267 - 280, 31.12.2023

Novsheena Rasool , Javaid Iqbal Bhat

https://doi.org/10.51537/chaos.1326790

Cited By: 4

Abstract

Recent advancements in deep learning, particularly convolutional networks, have rapidly become the preferred methodology for analyzing medical images, facilitating tasks like disease segmentation, classification, and pattern quantification. Central to these advancements is the capacity to leverage hierarchical feature representations acquired solely from data. This comprehensive review meticulously examines a variety of deep learning techniques applied across diverse healthcare domains, delving into the intricate realm of medical imaging to unveil concealed patterns through strategic deep learning methodologies. Encompassing a range of diseases, including Alzheimer’s, breast cancer, brain tumors, glaucoma, heart murmurs, retinal microaneurysms, colorectal liver metastases, and more, the analysis emphasizes contributions succinctly summarized in a tabular form.The table provides an overview of various deep learning approaches applied to different diseases, incorporating methodologies, datasets, and outcomes for each condition.Notably, performance metrics such as accuracy, specificity, sensitivity, and other crucial measures underscore the achieved results. Specifically, an in-depth discussion is conducted on the Convolutional Neural Network (CNN) owing to its widespread adoption as a paramount tool in computer vision tasks. Moreover, an exhaustive exploration encompasses deep learning classification approaches, procedural aspects of medical image processing, as well as a thorough examination of key features and characteristics. At the end, we delve into a range of research challenges and put forth potential avenues for future improvements in the field.

Keywords

Deep Learning, Complexity, CNN, Medical image analysis, Pattern recognition, Segmentation

References

• Abiwinanda, N., M. Hanif, S. T. Hesaputra, A. Handayani, and T. R. Mengko, 2019 Brain tumor classification using convolutional neural network. In World Congress on Medical Physics and Biomedical Engineering 2018: June 3-8, 2018, Prague, Czech Republic (Vol. 1), pp. 183–189, Springer.
• Ahmad, H. M., M. J. Khan, A. Yousaf, S. Ghuffar, and K. Khurshid, 2020 Deep learning: a breakthrough in medical imaging. Current Medical Imaging 16: 946–956.
• Ahuja, S., B. K. Panigrahi, and T. K. Gandhi, 2022 Enhanced performance of dark-nets for brain tumor classification and segmentation using colormap-based superpixel techniques. Machine Learning with Applications 7: 100212.
• Albarqouni, S., C. Baur, F. Achilles, V. Belagiannis, S. Demirci, et al., 2016 Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE transactions on medical imaging 35: 1313–1321.
• Alwzwazy, H. A., H. M. Albehadili, Y. S. Alwan, and N. E. Islam, 2016 Handwritten digit recognition using convolutional neural networks. International Journal of Innovative Research in Computer and Communication Engineering 4: 1101–1106.
• Alzubaidi, L., J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, et al., 2021 Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions. Journal of big Data 8: 1–74.
• Arif, M., F. Ajesh, S. Shamsudheen, O. Geman, D. Izdrui, et al., 2022 Brain tumor detection and classification by mri using biologically inspired orthogonal wavelet transform and deep learning techniques. Journal of Healthcare Engineering 2022.
• Awotunde, J. B., N. S. Sur, A. L. Imoize, S. Misra, and T. Gaber, 2022 An enhanced residual networks based framework for early alzheimer’s disease classification and diagnosis. In International Conference on Communication, Devices and Networking, pp. 335– 348, Springer.
• Bhattacharjee, A., R. Murugan, T. Goel, and S. Mirjalili, 2023 Pulmonary nodule segmentation framework based on fine-tuned and pretrained deep neural network using ct images. IEEE Transactions on Radiation and Plasma Medical Sciences 7: 394–409.
• Chen, C., Y.Wang, J. Niu, X. Liu, Q. Li, et al., 2021 Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Transactions on Medical Imaging 40: 2439–2451.
• Chen, D. and B. K.-W. Mak, 2015 Multitask learning of deep neural networks for low-resource speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 23: 1172–1183.
• Cheplygina, V., M. de Bruijne, and J. P. Pluim, 2019 Not-sosupervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Medical image analysis 54: 280–296.
• Cobo, M., P. Menéndez Fernández-Miranda, G. Bastarrika, and L. Lloret Iglesias, 2023 Enhancing radiomics and deep learning systems through the standardization of medical imaging workflows. Scientific Data 10: 732.
• Costanzo, S., A. Flores, and G. Buonanno, 2023 Fast and accurate cnn-based machine learning approach for microwave medical imaging in cancer detection. IEEE Access .
• Dai, L., R. Fang, H. Li, X. Hou, B. Sheng, et al., 2018 Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE transactions on medical imaging 37: 1149–1161.
• Dominguez-Morales, J. P., A. F. Jimenez-Fernandez, M. J. Dominguez-Morales, and G. Jimenez-Moreno, 2017 Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors. IEEE transactions on biomedical circuits and systems 12: 24–34.
• Dong, H., G. Yang, F. Liu, Y. Mo, and Y. Guo, 2017 Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings 21, pp. 506–517, Springer.
• Du, M., F. Li, G. Zheng, and V. Srikumar, 2017 Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp. 1285–1298.
• Esteban, C., S. L. Hyland, and G. Rätsch, 2017 Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633 .
• Esteva, A., B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, et al., 2017 Dermatologist-level classification of skin cancer with deep neural networks. nature 542: 115–118.
• Feng, C., A. Elazab, P. Yang, T. Wang, F. Zhou, et al., 2019 Deep learning framework for alzheimer’s disease diagnosis via 3d-cnn and fsbi-lstm. IEEE Access 7: 63605–63618.
• Fu, H., J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, et al., 2018 Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE transactions on medical imaging 37: 1597–1605.
• Gulshan, V., L. Peng, M. Coram, M. C. Stumpe, D. Wu, et al., 2016 Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama 316: 2402–2410.
• Islam, J. and Y. Zhang, 2018 Brain mri analysis for alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain informatics 5: 1–14.
• Johnson, J. M. and T. M. Khoshgoftaar, 2019 Survey on deep learning with class imbalance. Journal of Big Data 6: 1–54.
• LeCun, Y., Y. Bengio, and G. Hinton, 2015 Deep learning. nature 521: 436–444.
• Li, A., T. Luo, Z. Lu, T. Xiang, and L. Wang, 2019 Large-scale few-shot learning: Knowledge transfer with class hierarchy. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pp. 7212–7220.
• Li, C., Z. Xi, G. Jin, W. Jiang, B. Wang, et al., 2023 Deep-learningenabled microwave-induced thermoacoustic tomography based on resattu-net for transcranial brain hemorrhage detection. IEEE Transactions on Biomedical Engineering .
• Li, Q., W. Cai, X. Wang, Y. Zhou, D. D. Feng, et al., 2014 Medical image classification with convolutional neural network. In 2014 13th international conference on control automation robotics & vision (ICARCV), pp. 844–848, IEEE.
• Lian, C., M. Liu, J. Zhang, and D. Shen, 2018 Hierarchical fully convolutional network for joint atrophy localization and alzheimer’s disease diagnosis using structural mri. IEEE transactions on pattern analysis and machine intelligence 42: 880–893.
• Lipton, Z. C., D. C. Kale, C. Elkan, and R. Wetzel, 2015 Learning to diagnose with lstm recurrent neural networks. arXiv preprint arXiv:1511.03677 .
• Liu, J., B. Xu, C. Zheng, Y. Gong, J. Garibaldi, et al., 2018 An endto- end deep learning histochemical scoring system for breast cancer tma. IEEE transactions on medical imaging 38: 617–628.
• Liu, M., M. Zhou, T. Zhang, and N. Xiong, 2020 Semi-supervised learning quantization algorithm with deep features for motor imagery eeg recognition in smart healthcare application. Applied Soft Computing 89: 106071.
• Liu, Z., L. Tong, L. Chen, Z. Jiang, F. Zhou, et al., 2023 Deep learning based brain tumor segmentation: a survey. Complex & intelligent systems 9: 1001–1026.
• Liu, Z., L. Tong, L. Chen, F. Zhou, Z. Jiang, et al., 2021 Canet: Context aware network for brain glioma segmentation. IEEE Transactions on Medical Imaging 40: 1763–1777.
• Meena, T. and S. Roy, 2022 Bone fracture detection using deep supervised learning from radiological images: A paradigm shift. Diagnostics 12: 2420.
• Mimboro, P., A. Sunyoto, and R. S. Kharisma, 2021 Segmentation of brain tumor objects in magnetic resonance imaging (mri) image using connected component label algorithm. In 2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), pp. 195–198, IEEE.
• Miotto, R., L. Li, B. A. Kidd, and J. T. Dudley, 2016 Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific reports 6: 1–10.
• Muhammad, K., S. Khan, J. Del Ser, and V. H. C. De Albuquerque, 2020 Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE Transactions on Neural Networks and Learning Systems 32: 507–522.
• Mumuni, A. and F. Mumuni, 2022 Data augmentation: A comprehensive survey of modern approaches. Array p. 100258.
• Pang, G., C. Shen, L. Cao, and A. V. D. Hengel, 2021 Deep learning for anomaly detection: A review. ACM computing surveys (CSUR) 54: 1–38.
• Pereira, S., A. Pinto, V. Alves, and C. A. Silva, 2016 Brain tumor segmentation using convolutional neural networks in mri images. IEEE transactions on medical imaging 35: 1240–1251.
• Rajkomar, A., E. Oren, K. Chen, A. M. Dai, N. Hajaj, et al., 2018 Scalable and accurate deep learning with electronic health records. NPJ digital medicine 1: 18.
• Rajpurkar, P., J. Irvin, R. L. Ball, K. Zhu, B. Yang, et al., 2018 Deep learning for chest radiograph diagnosis: A retrospective comparison of the chexnext algorithm to practicing radiologists. PLoS medicine 15: e1002686.
• Rajput, S., R. A. Kapdi, M. S. Raval, and M. Roy, 2023 Interpretable machine learning model to predict survival days of malignant brain tumor patients. Machine Learning: Science and Technology 4: 025025.
• Ramzan, M., M. Habib, and S. A. Khan, 2022 Secure and efficient privacy protection system for medical records. Sustainable Computing: Informatics and Systems 35: 100717.
• Rasool, N. and J. I. Bhat, 2023 Glioma brain tumor segmentation using deep learning: A review. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 484–489, IEEE.
• Saba, T., A. S. Mohamed, M. El-Affendi, J. Amin, and M. Sharif, 2020 Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research 59: 221–230.
• Sarraf, S. and G. Tofighi, 2016 Deep learning-based pipeline to recognize alzheimer’s disease using fmri data. In 2016 future technologies conference (FTC), pp. 816–820, IEEE.
• Sharma, N. and L. M. Aggarwal, 2010 Automated medical image segmentation techniques.
• Shen, T., J. Wang, C. Gou, and F.-Y. Wang, 2020 Hierarchical fused model with deep learning and type-2 fuzzy learning for breast cancer diagnosis. IEEE Transactions on Fuzzy Systems 28: 3204– 3218.
• Shin, H.-C., K. Roberts, L. Lu, D. Demner-Fushman, J. Yao, et al., 2016 Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2497–2506.
• Shubham, S., N. Jain, V. Gupta, S. Mohan, M. M. Ariffin, et al., 2023 Identify glomeruli in human kidney tissue images using a deep learning approach. Soft Computing 27: 2705–2716.
• Sreng, S., N. Maneerat, K. Hamamoto, and K. Y. Win, 2020 Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Applied Sciences 10: 4916.
• Tan, C., F. Sun, T. Kong,W. Zhang, C. Yang, et al., 2018 A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279, Springer.
• Tang, Z., Y. Xu, L. Jin, A. Aibaidula, J. Lu, et al., 2020 Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients. IEEE transactions on medical imaging 39: 2100–2109.
• Trajanovski, S., C. Shan, P. J.Weijtmans, S. G. B. de Koning, and T. J. Ruers, 2020 Tongue tumor detection in hyperspectral images using deep learning semantic segmentation. IEEE transactions on biomedical engineering 68: 1330–1340.
• Vorontsov, E., M. Cerny, P. Régnier, L. Di Jorio, C. J. Pal, et al., 2019 Deep learning for automated segmentation of liver lesions at ct in patients with colorectal cancer liver metastases. Radiology: Artificial Intelligence 1: 180014.
• Wang, H. and B. Raj, 2017 On the origin of deep learning. arXiv preprint arXiv:1702.07800 .
• Wu, N., J. Phang, J. Park, Y. Shen, Z. Huang, et al., 2019 Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE transactions on medical imaging 39: 1184–1194.
• Yang, S., F. Zhu, X. Ling, Q. Liu, and P. Zhao, 2021 Intelligent health care: Applications of deep learning in computational medicine. Frontiers in Genetics 12: 607471.
• Zahoor, M. M., S. A. Qureshi, S. Bibi, S. H. Khan, A. Khan, et al., 2022 A new deep hybrid boosted and ensemble learning-based brain tumor analysis using mri. Sensors 22: 2726.
• Zeineldin, R. A., M. E. Karar, J. Coburger, C. R. Wirtz, and O. Burgert, 2020 Deepseg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance flair images. International journal of computer assisted radiology and surgery 15: 909–920.
• Zhang, D., G. Huang, Q. Zhang, J. Han, J. Han, et al., 2020 Exploring task structure for brain tumor segmentation from multi-modality mr images. IEEE Transactions on Image Processing 29: 9032– 9043.
• Zhang, Y., D. Sidibé, O. Morel, and F. Mériaudeau, 2021 Deep multimodal fusion for semantic image segmentation: A survey. Image and Vision Computing 105: 104042.
• Zhao, T., Z. Sun, Y. Guo, Y. Sun, Y. Zhang, et al., 2023 Automatic renal mass segmentation and classification on ct images based on 3d u-net and resnet algorithms. Frontiers in Oncology 13: 1169922.
• Zheng, J., D. Lin, Z. Gao, S.Wang, M. He, et al., 2020 Deep learning assisted efficient adaboost algorithm for breast cancer detection and early diagnosis. IEEE Access 8: 96946–96954.
• Zhu, J., J. Ye, L. Dong, X. Ma, N. Tang, et al., 2023 Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal mri radiomics. International Journal of Imaging Systems and Technology .
• Zhu, W., L. Sun, J. Huang, L. Han, and D. Zhang, 2021 Dual attention multi-instance deep learning for alzheimer’s disease diagnosis with structural mri. IEEE Transactions on Medical Imaging 40: 2354–2366.