Real-Time Imbalance Liver Tumor Sensor Databases: A Deep Classification Framework with Ensemble Feature Extraction, Ranking, and Probabilistic Segmentation for Efficient Analysis


  • N. Nanda Prakash Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, India.
  • V. Rajesh Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, India.
  • Sandeep Dwarkanath Pande School of Computer Engineering, MIT Academy of Engineering, Alandi, Pune. Dist., Pune, Maharashtra–412105, India.
  • Syed Inthiyaz Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, India.
  • Sk Hasane Ahammad Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522302, India.
  • Dharmesh Dhabliya Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India.
  • Rahul Joshi Associate Professor, Computer Science and Engineering, Symbiosis Institute of Technology, Pune, Symbiosis International (Deemed University), Pune - 412115, India.


Imbalance liver image data, probabilistic segmentation, deep learning, support vector machine, decision tree, ensemble learning model


As the size of liver tumor image databases increases, it becomes challenging to enhance the true positive rate of traditional prediction approaches due to the high majority-minority ratio and noise. However, while 3D convolutions have the potential to fully leverage spatial information, they also come with high computational costs and require significant GPU memory usage. On the other hand, 2D convolutions are limited in their ability to utilize the information contained in the third dimension. Missing feature values, feature noise, and Imbalanced liver classes are some of the significant factors that can impact the quality of input data. The quality of imbalance data significantly impacts the efficiency of classification approaches, making it necessary to ensure high-quality input data to achieve optimal results. Therefore, to ensure high-quality predictions on imbalanced liver datasets, models need to be optimized. Sensors are commonly used to collect and measure physical parameters, and they can be used to obtain liver tumor data for the proposed model. In this work, medical imaging sensors such as CT (computed tomography) machines are used to capture detailed images of the liver and identify potential tumors. Therefore, sensors play a crucial role in the proposed model by providing the necessary data to extract features, segment the liver and detect tumors accurately. In this work, an optimized k-joint probabilistic segmentation-based ensemble classification model is proposed to address the issues of homogenous and heterogenous liver tumor detection. Additionally, novel image filtering, feature extraction and ranking approaches are proposed to improve the imbalanced liver tumor regions for classification process. The experimental results demonstrate that the proposed classification model based on k-joint probability segmentation has significantly improved the accuracy, recall, precision, and AUC compared to the existing models.


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P. Lv, J. Wang, and H. Wang, “2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT,” Biomedical Signal Processing and Control, vol. 75, p. 103567, May 2022, doi: 10.1016/j.bspc.2022.103567.

Q. Zhang, Y. Liang, Y. Zhang, Z. Tao, R. Li, and H. Bi, “A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation,” International Journal of Medical Informatics, vol. 171, p. 104984, Mar. 2023, doi: 10.1016/j.ijmedinf.2023.104984.

R. Rong et al., “A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization,” Modern Pathology, p. 100196, Apr. 2023, doi: 10.1016/j.modpat.2023.100196.

Y. Chen et al., “A deep residual attention-based U-Net with a biplane joint method for liver segmentation from CT scans,” Computers in Biology and Medicine, vol. 152, p. 106421, Jan. 2023, doi: 10.1016/j.compbiomed.2022.106421.

G. Tong and H. Jiang, “A hard segmentation network guided by soft segmentation for tumor segmentation on PET/CT images,” Biomedical Signal Processing and Control, vol. 85, p. 104918, Aug. 2023, doi: 10.1016/j.bspc.2023.104918.

M. O. Khairandish, M. Sharma, V. Jain, J. M. Chatterjee, and N. Z. Jhanjhi, “A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images,” IRBM, vol. 43, no. 4, pp. 290–299, Aug. 2022, doi: 10.1016/j.irbm.2021.06.003.

O. Alpar, “A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging,” Expert Systems with Applications, vol. 216, p. 119462, Apr. 2023, doi: 10.1016/j.eswa.2022.119462.

Z. Diao, H. Jiang, and T. Shi, “A unified uncertainty network for tumor segmentation using uncertainty cross entropy loss and prototype similarity,” Knowledge-Based Systems, vol. 246, p. 108739, Jun. 2022, doi: 10.1016/j.knosys.2022.108739.

G. Chen et al., “An improved 3D KiU-Net for segmentation of liver tumor,” Computers in Biology and Medicine, p. 107006, May 2023, doi: 10.1016/j.compbiomed.2023.107006.

J. Zhang, H. Jiang, and T. Shi, “ASE-Net: A tumor segmentation method based on image pseudo enhancement and adaptive-scale attention supervision module,” Computers in Biology and Medicine, vol. 152, p. 106363, Jan. 2023, doi: 10.1016/j.compbiomed.2022.106363.

R. Ranjbarzadeh and S. B. Saadi, “Automated liver and tumor segmentation based on concave and convex points using fuzzy c-means and mean shift clustering,” Measurement, vol. 150, p. 107086, Jan. 2020, doi: 10.1016/j.measurement.2019.107086.

G. Z. Ferl et al., “Automated segmentation of lungs and lung tumors in mouse micro-CT scans,” iScience, vol. 25, no. 12, p. 105712, Dec. 2022, doi: 10.1016/j.isci.2022.105712.

R. R. Savjani, M. Lauria, S. Bose, J. Deng, Y. Yuan, and V. Andrearczyk, “Automated Tumor Segmentation in Radiotherapy,” Seminars in Radiation Oncology, vol. 32, no. 4, pp. 319–329, Oct. 2022, doi: 10.1016/j.semradonc.2022.06.002.

R. V. Manjunath and K. Kwadiki, “Automatic liver and tumour segmentation from CT images using Deep learning algorithm,” Results in Control and Optimization, vol. 6, p. 100087, Mar. 2022, doi: 10.1016/j.rico.2021.100087.

A. Qayyum, A. Lalande, and F. Meriaudeau, “Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging,” Computers in Biology and Medicine, vol. 127, p. 104097, Dec. 2020, doi: 10.1016/j.compbiomed.2020.104097.

Y. Ren, D. Zou, W. Xu, X. Zhao, W. Lu, and X. He, “Bimodal segmentation and classification of endoscopic ultrasonography images for solid pancreatic tumor,” Biomedical Signal Processing and Control, vol. 83, p. 104591, May 2023, doi: 10.1016/j.bspc.2023.104591.

R. Vankdothu and M. A. Hameed, “Brain tumor MRI images identification and classification based on the recurrent convolutional neural network,” Measurement: Sensors, vol. 24, p. 100412, Dec. 2022, doi: 10.1016/j.measen.2022.100412.

Daher, M. G., Trabelsi, Y., Ahmed, N. M., Prajapati, Y. K., Sorathiya, V., Ahammad, S. H., ... & Rashed, A. N. Z. (2022). Detection of basal cancer cells using photodetector based on a novel surface plasmon resonance nanostructure employing perovskite layer with an ultra high sensitivity. Plasmonics, 17(6), 2365-2373.

Reddy, A. P. C., Kumar, M. S., Krishna, B. M., Inthiyaz, S., & Ahammad, S. H. (2019). Physical unclonable function based design for customized digital logic circuit. International Journal of Advanced Science and Technology, 28(8), 206-221.

Ü. Budak, Y. Guo, E. Tanyildizi, and A. Şengür, “Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation,” Medical Hypotheses, vol. 134, p. 109431, Jan. 2020, doi: 10.1016/j.mehy.2019.109431.

X. Wang, S. Wang, Z. Zhang, X. Yin, T. Wang, and N. Li, “CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentation,” Biomedical Signal Processing and Control, vol. 79, p. 104258, Jan. 2023, doi: 10.1016/j.bspc.2022.104258.

Zuhayer, A., Abd-Elnaby, M., Ahammad, S. H., Eid, M. M., Sorathiya, V., & Rashed, A. N. Z. (2022). A Gold-Plated Twin Core D-Formed Photonic Crystal Fiber (PCF) for Ultrahigh Sensitive Applications Based on Surface Plasmon Resonance (SPR) Approach. Plasmonics, 17(5), 2089-2101.




How to Cite

Prakash, N. N. ., Rajesh, V. ., Pande, S. D. ., Inthiyaz, S. ., Ahammad, S. H. ., Dhabliya, D. ., & Joshi, R. . (2024). Real-Time Imbalance Liver Tumor Sensor Databases: A Deep Classification Framework with Ensemble Feature Extraction, Ranking, and Probabilistic Segmentation for Efficient Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 11–22. Retrieved from



Research Article