Enhancing Diagnostic Accuracy: AI-Driven Solutions In Medical Imaging
Keywords:
Hybrid neural networks, medical imaging, CNN-LSTM, RNN-LSTM, diagnostic accuracy
Abstract
AI has improved medical imaging diagnosis accuracy and efficiency. This research proposes an AI-driven hybrid CNN and transformer-based architecture for medical imaging diagnostics. This study investigates the use of hybrid neural network models, specifically CNN-LSTM and RNN-LSTM, to enhance diagnostic accuracy in medical imaging. By combining the spatial feature extraction capabilities of Convolutional Neural Networks (CNNs) with the temporal sequence analysis strengths of Long Short-Term Memory (LSTM) networks, these hybrid models offer superior performance in image analysis tasks. The CNN-LSTM model achieved the highest performance metrics, with an accuracy of 92.1%, precision of 91.8%, recall of 92.3%, and an F1 score of 92.0%. Similarly, the RNN-LSTM model also showed excellent results, with an accuracy of 91.4%, precision of 90.9%, recall of 91.7%, and an F1 score of 91.3%. These findings demonstrate the potential of hybrid models to significantly improve diagnostic accuracy compared to traditional CNN, RNN, and standalone LSTM models. The study underscores the importance of integrating advanced AI techniques in medical imaging to achieve better diagnostic outcomes and enhance patient care. Future work will focus on expanding datasets, exploring advanced hybrid architectures, and validating these models in clinical settings to ensure their practical utility and impact.
References
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39. Raut G, Raut A, Bhagade J, Bhagade J, Gavhane S. Deep learning approach for brain tumor detection and segmentation. In2020 International Conference on Convergence to Digital World-Quo Vadis (ICCDW) 2020 Feb 18 (pp. 1-5). IEEE
40. Bi X, Zhang C, Zhao X, Li D, Sun Y, Ma Y. CODES: Efficient incremental semi-supervised classification over drifting and evolving social streams. IEEE Access. 2020 Jan 10;8:14024-35.
41. Tang S, Wang C, Nie J, Kumar N, Zhang Y, Xiong Z, Barnawi A. EDL-COVID: Ensemble deep learning for COVID-19 case detection from chest X-ray images. IEEE Transactions on Industrial Informatics. 2021 Feb 8;17(9):6539-49.
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44. Someswararao C, Shankar RS, Appaji SV, Gupta VM. Brain tumor detection model from MR images using convolutional neural network. In2020 International conference on system, computation, automation and networking (ICSCAN) 2020 Jul 3 (pp. 1-4). IEEE.
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48. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. InProceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 4700-4708
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50. Iqbal MJ, Iqbal MW, Anwar M, Khan MM, Nazimi AJ, Ahmad MN. Brain tumor segmentation in multimodal MRI using U-Net layered structure. Comput Mater Contin. 2022;74(3):5267-81
51. Ahmed YA, Anwar M, Iqbal MW, Khan MM, Malik MS, Ejaz K. ‘Sentiment analysis using deep learning techniques: A review. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition). 2023;42(03):1671-5497.
52. Dupre R, Fajtl J, Argyriou V, Remagnino P. Improving dataset volumes and model accuracy with semi-supervised iterative self-learning. IEEE Transactions on Image Processing. 2019 May 6;29:4337-48.
53. Uppal M, Gupta D, Juneja S, Dhiman G, Kautish S. Cloud‐based fault prediction using IoT in office automation for improvisation of health of employees. Journal of Healthcare Engineering. 2021;2021(1):8106467
54. Yildirim M, Çinar A. Classification of white blood cells by deep learning methods for diagnosing disease. Rev. d'Intelligence Artif.. 2019 Nov;33(5):335-40.
55. Dhiman G, Juneja S, Viriyasitavat W, Mohafez H, Hadizadeh M, Islam MA, El Bayoumy I, Gulati K. A novel machine-learning-based hybrid CNN model for tumor identification in medical image processing. Sustainability. 2022 Jan 27;14(3):1447
2. Arasi PR, Suganthi M. A clinical support system for brain tumor classification using soft computing techniques. Journal of medical systems. 2019 May;43(5):144.
3. Yildirim M, Çinar A. Classification of white blood cells by deep learning methods for diagnosing disease. Rev. d'Intelligence Artif. 2019 Nov;33(5):335-40.
4. Ali G, Dastgir A, Iqbal MW, Anwar M, Faheem M. A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images. IEEE Journal of Translational Engineering in Health and Medicine. 2023 Jun 1; 11:341-50.
5. Shahzadi I, Tang TB, Meriadeau F, Quyyum A. CNN-LSTM: Cascaded framework for brain tumour classification. In2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) 2018 Dec 3 (pp. 633-637). IEEE.
6. El Abbadi NK, Kadhim NE. Brain tumor classification based on singular value decomposition. Brain. 2016 Aug;5(8):10-7148
7. Thawkar S, Sharma S, Khanna M, kumar Singh L. Breast cancer prediction using a hybrid method based on butterfly optimization algorithm and ant lion optimizer. Computers in Biology and Medicine. 2021 Dec 1; 139:104968.
8. Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M. Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research. 2020 Jan 1; 59:221-30.
9. Arasi PR, Suganthi M. A clinical support system for brain tumor classification using soft computing techniques. Journal of medical systems. 2019 May;43(5):144.
10. Shahzadi I, Tang TB, Meriadeau F, Quyyum A. CNN-LSTM: Cascaded framework for brain tumour classification. In2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) 2018 Dec 3 (pp. 633-637). IEEE.
11. El Abbadi NK, Kadhim NE. Brain tumor classification based on singular value decomposition. Brain. 2016 Aug;5(8):10-7148
12. Pitchai R, Supraja P, Victoria AH, Madhavi MJ. RETRACTED ARTICLE: Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images. Neural Processing Letters. 2021 Aug;53(4):2519-32.
13. Sharif MI, Khan MA, Alhussein M, Aurangzeb K, Raza M. A decision support system for multimodal brain tumor classification using deep learning. Complex & Intelligent Systems. 2021 Mar 9:1-4.
14. Sopharak A, Uyyanonvara B. Automatic exudates detection from diabetic retinopathy retinal image using fuzzy c-means and morphological methods. InConf. on Advances in Computer Science and Technology 2007 Apr 2 (pp. 359-364).
15. Vallabha D, Dorairaj R, Namuduri K, Thompson H. Automated detection and classification of vascular abnormalities in diabetic retinopathy. InConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. 2004 Nov 7 (Vol. 2, pp. 1625-1629). IEEE.
16. Li H, Hsu W, Lee ML, Wong TY. Automatic grading of retinal vessel caliber. IEEE Transactions on Biomedical Engineering. 2005 Jun 13;52(7):1352-5.
17. Chapelle O, Sindhwani V, Keerthi SS. Optimization techniques for semi-supervised support vector machines. Journal of Machine Learning Research. 2008 Feb 1;9(2).
18. Zhu H, Wei L, Niu P. The novel coronavirus outbreak in Wuhan, China. Global health research and policy. 2020 Dec;5:1-3.
19. Khadka A, Remagnino P, Argyriou V. Synthetic crowd and pedestrian generator for deep learning problems. InICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 May 4 (pp. 4052-4056). IEEE.
20. Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. Ieee Access. 2017 Dec 29;6:9375-89.
21. Cosma G, Brown D, Archer M, Khan M, Pockley AG. A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert systems with applications. 2017 Mar 15;70:1-9.
22. Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence. 2002 Jul;24(7):971-87.
23. Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004 Nov;60:91-110.
24. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) 2005 Jun 20 (Vol. 1, pp. 886-893). Ieee.
25. Afshar P, Mohammadi A, Plataniotis KN. Brain tumor type classification via capsule networks. In2018 25th IEEE international conference on image processing (ICIP) 2018 Oct 7 (pp. 3129-3133). IEEE.
26. Technological Research (ICETIETR) 2018 Jul 11 (pp. 1-4). IEEE.
27. Wang R, Chaudhari P, Davatzikos C. Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation. Medical image analysis. 2022 Feb 1;76:102309.
28. Arokia Jesu Prabhu L, Jayachandran A. Mixture model segmentation system for parasagittal meningioma brain tumor classification based on hybrid feature vector. Journal of medical systems. 2018 Dec;42(12):251.
29. Dupre R, Fajtl J, Argyriou V, Remagnino P. Improving dataset volumes and model accuracy with semi-supervised iterative self-learning. IEEE Transactions on Image Processing. 2019 May 6;29:4337-48.
30. Ali G, Dastgir A, Iqbal MW, Anwar M, Faheem M. A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images. IEEE Journal of Translational Engineering in Health and Medicine. 2023 Jun 1;11:341-50
31. Kumar RL, Kakarla J, Isunuri BV, Singh M. Multi-class brain tumor classification using residual network and global average pooling. Multimedia Tools and Applications. 2021 Apr;80(9):13429-38.
32. Rammurthy D, Mahesh PK. Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. Journal of King Saud University-Computer and Information Sciences. 2022 Jun 1;34(6):3259-72.
33. Deb D, Roy S. Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization. Multimedia tools and applications. 2021 Jan;80(2):2621-45.
34. Yin B, Wang C, Abza F. New brain tumor classification method based on an improved version of whale optimization algorithm. Biomedical Signal Processing and Control. 2020 Feb 1;56:101728.
35. Sultan HH, Salem NM, Al-Atabany W. Multi-classification of brain tumor images using deep neural network. IEEE access. 2019 May 27;7:69215-25.
36. Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, Ai J, Shi Q, Zhang Y, Jin S, Zhang L. COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations. IEEE transactions on big data. 2021 Feb 2;7(1):13-24
37. Prakash RM, Kumari RS. Classification of MR brain images for detection of tumor with transfer learning from pre-trained CNN models. In2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) 2019 Mar 21 (pp. 508-511). IEEE
38. Chaudhary Y, Mehta M, Sharma R, Gupta D, Khanna A, Rodrigues JJ. Efficient-CovidNet: deep learning based COVID-19 detection from chest x-ray images. In2020 IEEE international conference on e-health networking, application & services (HEALTHCOM) 2021 Mar 1 (pp. 1-6). IEEE.
39. Raut G, Raut A, Bhagade J, Bhagade J, Gavhane S. Deep learning approach for brain tumor detection and segmentation. In2020 International Conference on Convergence to Digital World-Quo Vadis (ICCDW) 2020 Feb 18 (pp. 1-5). IEEE
40. Bi X, Zhang C, Zhao X, Li D, Sun Y, Ma Y. CODES: Efficient incremental semi-supervised classification over drifting and evolving social streams. IEEE Access. 2020 Jan 10;8:14024-35.
41. Tang S, Wang C, Nie J, Kumar N, Zhang Y, Xiong Z, Barnawi A. EDL-COVID: Ensemble deep learning for COVID-19 case detection from chest X-ray images. IEEE Transactions on Industrial Informatics. 2021 Feb 8;17(9):6539-49.
42. Hasegawa R, Iwamoto Y, Lin L, Hu H, Chen YW. Automatic segmentation of liver tumor in multiphase CT images by mask R-CNN. In2020 IEEE 2nd global conference on life sciences and technologies (LifeTech) 2020 Mar 10 (pp. 231-234). IEEE
43. Vijh S, Sharma S, Gaurav P. Brain tumor segmentation using OTSU embedded adaptive particle swarm optimization method and convolutional neural network. Data Visualization and Knowledge Engineering: Spotting Data Points with Artificial Intelligence. 2020:171-94.
44. Someswararao C, Shankar RS, Appaji SV, Gupta VM. Brain tumor detection model from MR images using convolutional neural network. In2020 International conference on system, computation, automation and networking (ICSCAN) 2020 Jul 3 (pp. 1-4). IEEE.
45. Yasir M, Hayat U, Rahman AU, Riaz F. Classification and Detection of Glioblastoma Tumor from MRI Images. In2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) 2021 Jan 12 (pp. 322-327). IEEE.
46. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017 May 24;60(6):84-90
47. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770-778).
48. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. InProceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 4700-4708
49. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 2818-2826).
50. Iqbal MJ, Iqbal MW, Anwar M, Khan MM, Nazimi AJ, Ahmad MN. Brain tumor segmentation in multimodal MRI using U-Net layered structure. Comput Mater Contin. 2022;74(3):5267-81
51. Ahmed YA, Anwar M, Iqbal MW, Khan MM, Malik MS, Ejaz K. ‘Sentiment analysis using deep learning techniques: A review. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition). 2023;42(03):1671-5497.
52. Dupre R, Fajtl J, Argyriou V, Remagnino P. Improving dataset volumes and model accuracy with semi-supervised iterative self-learning. IEEE Transactions on Image Processing. 2019 May 6;29:4337-48.
53. Uppal M, Gupta D, Juneja S, Dhiman G, Kautish S. Cloud‐based fault prediction using IoT in office automation for improvisation of health of employees. Journal of Healthcare Engineering. 2021;2021(1):8106467
54. Yildirim M, Çinar A. Classification of white blood cells by deep learning methods for diagnosing disease. Rev. d'Intelligence Artif.. 2019 Nov;33(5):335-40.
55. Dhiman G, Juneja S, Viriyasitavat W, Mohafez H, Hadizadeh M, Islam MA, El Bayoumy I, Gulati K. A novel machine-learning-based hybrid CNN model for tumor identification in medical image processing. Sustainability. 2022 Jan 27;14(3):1447
Published
2022-07-25
How to Cite
Jeevan Sreerama. (2022). Enhancing Diagnostic Accuracy: AI-Driven Solutions In Medical Imaging. Revista Electronica De Veterinaria, 23(4), 144-158. https://doi.org/10.69980/redvet.v23i4.871
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