Liver cancer is one of the most prevalent and lethal forms of cancer, making early detection crucial for effective treatment. This paper introduces a novel approach for automated liver tumor segmentation in computed tomography (CT) images by integrating a 3D U-Net architecture with the Bat Algorithm for hyperparameter optimization. The method enhances segmentation accuracy and robustness by intelligently optimizing key parameters like the learning rate and batch size. Evaluated on a publicly available dataset, our model demonstrates a strong ability to balance precision and recall, with a high F1-score at lower prediction thresholds. This is particularly valuable for clinical diagnostics, where ensuring no potential tumors are missed is paramount. Our work contributes to the field of medical image analysis by demonstrating that the synergy between a robust deep learning architecture and a metaheuristic optimization algorithm can yield a highly effective solution for complex segmentation tasks.
References
[1]
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A. and Jemal, A. (2018) Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 68, 394-424. https://doi.org/10.3322/caac.21492
[2]
Soler, L., Delingette, C., Malandain, S., Breaud, J.P. and Marescaux, J. (2001) Fully Automatic Anatomical, Pathological, and Functional Segmentation from CT Scans for Hepatic Surgery. Computer Aided Surgery, 6, 131-142.
[3]
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., et al. (2017) A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005
[4]
Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W. and Frangi, A., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Springer, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
[5]
Bengio, Y. (2012) Practical Recommendations for Gradient-Based Training of Deep Architectures. In: Montavon, G., Orr, G.B. and Müller, K.R., Eds., Neural Networks: Tricks of the Trade, Springer, 437-478. https://doi.org/10.1007/978-3-642-35289-8_26
[6]
Yang, X. (2010) A New Metaheuristic Bat-Inspired Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G. and Krasnogor, N., Eds., Nature Inspired Cooperative Strategies for Optimization, Springer, 65-74. https://doi.org/10.1007/978-3-642-12538-6_6
[7]
Bilic, P., Christ, P.F., Vorontsov, E., et al. (2020) The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis, 61, Article ID: 101656.
[8]
Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., et al. (2016) Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE Transactions on Medical Imaging, 35, 119-130. https://doi.org/10.1109/tmi.2015.2458702
[9]
Oktay, O., Schlemper, J., Folgoc, L.L., et al. (2018) Attention U-Net: Learning Where to Look for the Pancreas. arXiv: 1804.03999. https://doi.org/10.48550/arXiv.1804.03999
[10]
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N. and Liang, J. (2018) Unet++: A Nested U-Net Architecture for Medical Image Segmentation. In: Stoyanov, D., et al., Eds., Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 3-11. https://doi.org/10.1007/978-3-030-00889-5_1
[11]
Ibtehaz, N. and Rahman, M.S. (2020) Multiresunet: Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation. Neural Networks, 121, 74-87. https://doi.org/10.1016/j.neunet.2019.08.025
[12]
Christ, P.F., Ettlinger, F., Grün, F., et al. (2017) Automatic Liver and Tumor Segmentation of CT and MRI Volumes Using Cascaded Fully Convolutional Neural Networks. arXiv: 1702.05970. https://doi.org/10.48550/arXiv.1702.05970
[13]
Li, W., Jia, F. and Hu, Q. (2015) Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks. Journal of Computer and Communications, 3, 146-151.
[14]
Almotairi, S., Kareem, G., Aouf, M., Almutairi, B. and Salem, M.A. (2020) Liver Tumor Segmentation in CT Scans Using Modified SegNet. Sensors, 20, Article 1516. https://doi.org/10.3390/s20051516
[15]
Chlebus, G., Schenk, A., Moltz, J.H., van Ginneken, B., Hahn, H.K. and Meine, H. (2018) Automatic Liver Tumor Segmentation in CT with Fully Convolutional Neural Networks and Object-Based Postprocessing. Scientific Reports, 8, Article No. 15497. https://doi.org/10.1038/s41598-018-33860-7
[16]
Rahman, H., Bukht, T.F.N., Imran, A., Tariq, J., Tu, S. and Alzahrani, A. (2022) A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet. Bioengineering, 9, Article 368. https://doi.org/10.3390/bioengineering9080368
[17]
Jamshidi, B. and Rostamy-Malkhalifeh, M. (2024) Diagnosing, Classifying, and Predicting Brain Tumors from MRI Images Using VGG-19 and ANN Transfer Learning. Series of Internal Medicine, 1, 1-15.
[18]
Jamshidi, B., Ghorbani, N. and Rostamy-Malkhalifeh, M. (2025) Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA). Open Journal of Medical Imaging, 15, 106-136. https://doi.org/10.4236/ojmi.2025.153010
[19]
Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M. and Asari, V.K. (2018) Recurrent Residual Convolutional Neural Network Based on U-Net (R2U-Net) for Medical Image Segmentation. arXiv: 1802.06955.
[20]
Wang, H., Xie, S., Lin, L., Iwamoto, Y., Han, X.H., Chen, Y.W. and Tong, R. (2022) Mixed Transformer U-Net for Medical Image Segmentation. ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23-27 May 2022, 2390-2394.
[21]
Shehab, M., Abu-Hashem, M.A., Shambour, M.K.Y., Alsalibi, A.I., Alomari, O.A., Gupta, J.N.D., et al. (2022) A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization. Archives of Computational Methods in Engineering, 30, 765-797. https://doi.org/10.1007/s11831-022-09817-5
[22]
Naseer, I., Akram, S., Masood, T., Jaffar, A., Khan, M.A. and Mosavi, A. (2022) Performance Analysis of State-of-the-Art CNN Architectures for LUNA16. Sensors, 22, Article 4426. https://doi.org/10.3390/s22124426
[23]
Koti, M.S., Nagashree, B.A., Shreyas, K.P., Mathivanan, S.K., Dalu, G.T., et al. (2024) Lung Cancer Diagnosis Based on Weighted Convolutional Neural Network Using Gene Data Expression. Scientific Reports, 14, Article No. 3656. https://doi.org/10.1038/s41598-024-54124-7
[24]
Mamun, M., Mahmud, M.I., Meherin, M. and Abdelgawad, A. (2023) LCDctCNN: Lung Cancer Diagnosis of CT Scan Images Using CNN Based Model. 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 23-24 March 2023, 205-212. https://doi.org/10.1109/spin57001.2023.10116075
[25]
Causey, J.L., Zhang, J., Ma, S., Jiang, B., Qualls, J.A., Politte, D.G., et al. (2018) Highly Accurate Model for Prediction of Lung Nodule Malignancy with CT Scans. Scientific Reports, 8, Article No. 9286. https://doi.org/10.1038/s41598-018-27569-w
[26]
Xie, Y., Zhang, J., Xia, Y., Fulham, M. and Zhang, Y. (2018) Fusing Texture, Shape and Deep Model-Learned Information at Decision Level for Automated Classification of Lung Nodules on Chest CT. Information Fusion, 42, 102-110. https://doi.org/10.1016/j.inffus.2017.10.005
[27]
Shaffie, A., Soliman, A., Fraiwan, L., Ghazal, M., Taher, F., Dunlap, N., et al. (2018) A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules. Technology in Cancer Research & Treatment, 17, 1-9. https://doi.org/10.1177/1533033818798800
[28]
Potghan, S., Rajamenakshi, R. and Bhise, A. (2018) Multi-Layer Perceptron Based Lung Tumor Classification. 2018 SecondInternationalConferenceonElectronics, CommunicationandAerospaceTechnology (ICECA), Coimbatore, 29-31 March 2018, 499-502. https://doi.org/10.1109/iceca.2018.8474864
[29]
Singh, G.A.P. and Gupta, P.K. (2018) Performance Analysis of Various Machine Learning-Based Approaches for Detection and Classification of Lung Cancer in Humans. NeuralComputingandApplications, 31, 6863-6877. https://doi.org/10.1007/s00521-018-3518-x
[30]
Naqi, S.M., Sharif, M. and Jaffar, A. (2018) Lung Nodule Detection and Classification Based on Geometric Fit in Parametric Form and Deep Learning. NeuralComputingandApplications, 32, 4629-4647. https://doi.org/10.1007/s00521-018-3773-x
[31]
Shaffie, A., Soliman, A., Fraiwan, L., Ghazal, M., Taher, F., Dunlap, N., et al. (2018) A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules. TechnologyinCancerResearch&Treatment, 17. https://doi.org/10.1177/1533033818798800
[32]
Xie, Y., Zhang, J., Xia, Y., Fulham, M. and Zhang, Y. (2018) Fusing Texture, Shape and Deep Model-Learned Information at Decision Level for Automated Classification of Lung Nodules on Chest Ct. InformationFusion, 42, 102-110. https://doi.org/10.1016/j.inffus.2017.10.005
[33]
Mamun, M., Mahmud, M.I., Meherin, M. and Abdelgawad, A. (2023). LCDctCNN: Lung Cancer Diagnosis of CT Scan Images Using CNN Based Model. 2023 10thInternationalConferenceonSignalProcessingandIntegratedNetworks (SPIN), Noida, 23-24 March 2023, 205-212. https://doi.org/10.1109/spin57001.2023.10116075
[34]
Thangamani, M., Koti, M.S., Nagashree, B.A., Geetha, V., Shreyas, K.P., Mathivanan, S.K. and Dalu, G.T. (2024) Lung Cancer Diagnosis Based on Weighted Convolutional Neural Network Using Gene Data Expression. ScientificReports, 14, Article No. 3656. https://doi.org/10.1038/s41598-024-54124-7
[35]
Faheem, M., Khan, A.H., Zahra, N. and Gill, A.Y. (2025) Optimizing Liver Cancer Detection: Leveraging Gabor Features and Machine Learning for Enhanced Abdominal CT Image Segmentation. International Journal of Multidisciplinary Research and Growth Evaluation, 6, 224-230.
[36]
Colak, C., Yagin, F.H., Algarni, A., Algarni, A., Al-Hashem, F. and Ardigò, L.P. (2025) Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence. Medicina, 61, Article 405. https://doi.org/10.3390/medicina61030405
[37]
Xie, Y., Zhang, J. and Xia, Y. (2019) Semi-Supervised Adversarial Model for Benign-Malignant Lung Nodule Classification on Chest CT. MedicalImageAnalysis, 57, 237-248. https://doi.org/10.1016/j.media.2019.07.004
[38]
Halder, A., Chatterjee, S. and Dey, D. (2022) Adaptive Morphology Aided 2-Pathway Convolutional Neural Network for Lung Nodule Classification. BiomedicalSignalProcessingandControl, 72, Article 103347. https://doi.org/10.1016/j.bspc.2021.103347
[39]
Bushara, A.R., Vinod Kumar, R.S. and Kumar, S.S. (2023) LCD-Capsule Network for the Detection and Classification of Lung Cancer on Computed Tomography Images. MultimediaToolsandApplications, 82, 37573-37592. https://doi.org/10.1007/s11042-023-14893-1
[40]
Gautam, N., Basu, A. and Sarkar, R. (2023) Lung Cancer Detection from Thoracic CT Scans Using an Ensemble of Deep Learning Models. NeuralComputingandApplications, 36, 2459-2477. https://doi.org/10.1007/s00521-023-09130-7
