Citation

Mohd Zebaral Hoque, Jesmeen and Ab. Aziz, Nor Azlina and Abd Aziz, Azlan and Hossen, Md. Jakir and Ghazali, Anith Khairunnisa and Mohammed Tawsif Khan, Chy. (2025) Evaluating CNN Depth for Multi-Class Bone Tumor Tissue Classification in Histopathology Images. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

Artificial intelligence algorithms like the convolutional neural network (CNN) allow automated medical image analysis. However, accurate classification of tumour tissues in histopathological images including bone tumour poses significant challenges due to the subtle variations of the different classes. In this paper the impact of CNN architecture depth on the classification performance is evaluated. Additionally, the effectiveness of preprocessing techniques such as contrast enhancement and data augmentation is also studied. One of the key challenges faced in this study is the need to balance model complexity with computational efficiency while preventing overfitting on small and imbalanced datasets. The model was trained using a bone or osteosarcoma tumour dataset consists of 536 non-tumour tiles, 263 necrotic tumour tiles, and 345 viable tumour tiles. Our results demonstrate that custom-designed, lightweight CNN models can achieve competitive performance, with the best model achieving a validation accuracy of 87.44% and a validation loss of 0.3594, often outperforming deeper, pretrained architectures. This research contributes valuable insights into effective CNN architecture choices for histopathological analysis, specifically offering a pathway toward more efficient and accurate bone tumour classification systems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Convolutional neural networks
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 08:00
Last Modified:	19 Mar 2026 00:43
URII:	http://shdl.mmu.edu.my/id/eprint/15568

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