International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Based Bone Marrow Disorder Prediction Using Deep Learning |
|---|---|
| ABSTRACT | Bone marrow disorders such as Aplastic Anemia, Leukemia, Lymphoma, and Myelodysplastic Syndrome (MDS) are serious haematological diseases that require early and accurate diagnosis for effective treatment. Traditional diagnosis through microscopic examination is time-consuming, labor-intensive, and highly dependent on the expertise of hematologists. To address these challenges, this project presents an AI-Based Bone Marrow Disorder Detection System using deep learning techniques for automated disease classification and explainable diagnosis. The proposed system utilizes the YOLOv8 Classification model to classify bone marrow microscopic images into five categories: Aplastic Anemia, Leukemia, Lymphoma, Myelodysplastic Syndrome (MDS), and Normal. Before classification, the images are pre-processed through resizing and normalization to improve model performance. The trained model predicts the disease class and generates confidence scores for each category, enabling reliable and accurate diagnosis. To enhance the interpretability of the model, the system integrates ResNet18-based Multi-layer Grad-CAM++, which generates heatmaps highlighting the important regions of the image responsible for the prediction. These visual explanations help healthcare professionals understand the model's decision-making process and increase confidence in AI-assisted diagnosis. The proposed system aims to reduce diagnosis time, improve diagnostic accuracy, minimize human error, and support haematologists in clinical decision-making. It serves as an effective AI-assisted clinical decision-support tool for the early detection and classification of bone marrow disorders. |
| AUTHOR | KEERTHANA T C, YASHASWINI J IV Semester MCA, Dept. of DoS in Computer Science, PG Wing of SBRR Mahajana First Grade College (Autonomous), Mysuru, India Assistant Professor, Dept. of DoS in Computer Science, PG Wing of SBRR Mahajana First Grade College (Autonomous), Mysuru, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407036 |
| pdf/36_AI Based Bone Marrow Disorder Prediction Using Deep Learning.pdf | |
| KEYWORDS | |
| References | [1] A. Acevedo, M. Merino, S. Alférez, A. Molina, L. Boldú, and J. Rodellar, "A dataset of bone marrow cell images for morphological classification," Scientific Data, vol. 7, no. 1, 2020. [2] C. Matek, S. Schwarz, K. Spiekermann, and C. Marr, "Human-level recognition of blast cells in acute myeloid leukemia with deep learning," Nature Machine Intelligence, vol. 3, no. 2, pp. 160–168, 2021. [3] A. Rehman, M. A. Khan, M. Sharif, T. Saba, et al., "Deep learning-based automated detection of leukemia from microscopic images," IEEE Access, vol. 10, pp. 101234–101248, 2022. [4] Y. Zhang, X. Li, and H. Wang, "YOLO-based detection of abnormal cells in medical images," Computers in Biology and Medicine, vol. 134, 2021. [5] F. Wang, Y. Kaushal, and D. Khullar, "Artificial intelligence in hematology: Current applications and future perspectives," Frontiers in Medicine, vol. 7, 2020. [6] S. Hegde, P. Rao, and A. Kumar, "Medical image preprocessing techniques for automated disease detection," Journal of Medical Systems, vol. 46, no. 3, 2022. [7] X. Li, Y. Chen, and J. Wu, "YOLO-based real-time medical image analysis for cell detection," IEEE Transactions on Medical Imaging, vol. 42, no. 8, 2023. [8] R. Kumar, S. Verma, and P. Singh, "Artificial intelligence for automated hematological disease diagnosis," International Journal of Medical Informatics, vol. 151, 2021. [9] L. Chen, Y. Zhao, and H. Xu, "Deep learning for bone marrow image analysis," Biomedical Signal Processing and Control, vol. 75, 2022. [10] P. Singh, R. Sharma, and A. Gupta, "AI-assisted clinical decision support for bone marrow disorder diagnosis," Health Informatics Journal, vol. 29, no. 1, 2023. [11] Z. Lv et al., "High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system," Scientific Reports, vol. 13, Art. no. 13364, 2023. [12] H. Alshahrani, G. Sharma, V. Anand, and S. Gupta, "An intelligent attention-based transfer learning model for accurate differentiation of bone marrow stains to diagnose hematological disorder," Life, vol. 13, no. 10, p. 2091, 2023. [13] T. Ghete et al., "Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears," HemaSphere, vol. 8, no. 12, 2024. [14] L. van Eekelen, G. Litjens, and K. M. Hebeda, "Artificial intelligence in bone marrow histological diagnostics: Potential applications and challenges," Pathobiology, vol. 91, no. 1, pp. 8–17, 2024. [15] H. R. Chen et al., "Classification of hematologic malignancies from whole-slide bone marrow aspirates using a two-stage deep convolutional neural network pipeline," Digital Health, 2026. [16] S. Tripathi, A. I. Augustin, R. Sukumaran, S. Dheer, and E. Kim, "HematoNet: Expert-level classification of bone marrow cytology morphology in hematological malignancy with deep learning," SSRN Electronic Journal, 2022. [17] R. M. Tayebi et al., "Histogram of cell types: Deep learning for automated bone marrow cytology," arXiv preprint arXiv:2107.02293, 2021. [18] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. [19] R. R. Selvaraju et al., "Grad-CAM: Visual explanations from deep networks via gradient-based localization," in Proc. IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618–626. [20] G. Jocher, A. Chaurasia, and J. Qiu, "YOLO by Ultralytics," GitHub Repository, 2023. |
