Kidney Tumor Detection using CT Scan Images

[Audio] Our course project title is Kidney tumor detection using CT scan images..

[Audio] This is Our team Details.. Department of Computer Science and Engineering, KLE Technological University’s Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi.

[Audio] Our problem statement is The project aims to develop an efficient method to detect Human Kidney tumours using Machine Learning algorithms..

[Audio] Coming to motivation. If Kidney tumours are detected early, then can be treated more effectively and successfully saving more lives By developing a reliable system we contribute to enhancement of public health. The advanced tools like this can help doctors to make better-informed decisions about patient care. Early detection of tumors low risky treatments and better patient outcomes..

Objectives:. 1. Develop deep learning models for the detection and classification of kidney tumours in CT images. 2. Evaluate the performance of the developed models on a large dataset of CT images. 3. Compare the performance of the developed models to traditional methods of kidney tumour detection..

Scope:. Develop a deep learning model for accurate tumor detection in either CT scans, MRI images, or both. With image processing algorithms we will enhance tumor visibility and facilitate detection. Our model will contribute positively to the improvement of public health care. A robust kidney tumor detection system could potentially be implemented in various medical fields or research fields..

Dataset Description:. KLE TECHNOLOGICAL UNIVERSITY Belagavi Campus DR. M. S. SHESHGIRI COLLEGE OF ENGINEERING AND TECHNOLOGY.

Constraints:. KLE TECHNOLOGICAL UNIVERSITY Belagavi Campus DR. M. S. SHESHGIRI COLLEGE OF ENGINEERING AND TECHNOLOGY.

Model Architecture:. CNN Architecture:. VGG16 Architecture:.

Model Discussion:. KLE TECHNOLOGICAL UNIVERSITY Belagavi Campus DR. M. S. SHESHGIRI COLLEGE OF ENGINEERING AND TECHNOLOGY.

Model Discussion:. KLE TECHNOLOGICAL UNIVERSITY Belagavi Campus DR. M. S. SHESHGIRI COLLEGE OF ENGINEERING AND TECHNOLOGY.

Model Discussion:. KLE TECHNOLOGICAL UNIVERSITY Belagavi Campus DR. M. S. SHESHGIRI COLLEGE OF ENGINEERING AND TECHNOLOGY.

Conclusion: The solution involves training ML models to identify patterns indicating kidney tumors. The application of these models into the workflow of hospital’s diagnostic holds the capability to revolutionize detection of kidney tumor, offering a consistent and a faster approach. This novel fusion of medical proficiency and ML capabilities is visioned to transform current practices of healthcare, providing a more precise and efficient means of recognizing kidney tumors, eventually contributing to superior patient care..

Literature Survey:. D. A. Saleeb, R. M. Helmy, N. F. F. Areed, M. Marey, K. M. Almustafa, and A. S. Elkorany, “Detection of kidney cancer using circularly polarized patch antenna array,” IEEE Access, vol. 10, pp. 78102–78113 , 2022. D. Alzu’bi, M. Abdullah, I. Hmeidi, R. AlAzab, M. Gharaibeh, M. ElHeis, K. H. Almotairi, A. Forestiero, A. M. Hussein, and L. Abualigah, “Kidney tumor detection and classification based on deep learning approaches: a new dataset in ct scans,” Journal of Healthcare Engineering, vol. 2022, pp. 1–22, 2022. ] D.-Y. Kim and J.-W. Park, “Computer-aided detection of kidney tumor on abdominal computed tomography scans,” Acta radiologica, vol. 45 , no. 7, pp. 791–795, 2004..

Literature Survey:. Q. Yu, Y. Shi, J. Sun, Y. Gao, J. Zhu, and Y. Dai, “Crossbar-net: A novel convolutional neural network for kidney tumor segmentation in ct images,” IEEE Transactions on Image Processing, vol. 28, no. 8, pp. 4060–4074, 2019 J. Guo, W. Zeng, S. Yu, and J. Xiao, “Rau-net: U-net model based on residual and attention for kidney and kidney tumor segmentation,” in 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 2021, pp. 353–356. A. Skalski, J. Jakubowski, and T. Drewniak, “Kidney tumor segmentation and detection on computed tomography data,” in 2016 IEEE International Conference on Imaging Systems and Techniques (IST), 2016, pp. 238–242..

Literature Survey:. A. Hannan and P. Pal, “Detection and classification of kidney disease using convolutional neural networks,” J Neurol Neurorehab Res. 2023 ; 8 (2), vol. 136, 2023. X. Hou, C. Xie, F. Li, J. Wang, C. Lv, G. Xie, and Y. Nan, “A triplestage selfguided network for kidney tumor segmentation,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 341– 344. A. Abdelrahman and S. Viriri, “Fpn-se-resnet model for accurate diagnosis of kidney tumors using ct images,” Applied Sciences, vol. 13 , no. 17, p. 9802, 2023..

Literature Survey:. P. Rathnayaka, V. Jayasundara, R. Nawaratne, D. De Silva, W. Ranasinghe, and D. Alahakoon, “Kidney tumor detection using attention based u-net,” 2019. M. U. Emon, R. Islam, M. S. Keya, R. Zannat et al., “Performance analysis of chronic kidney disease through machine learning approaches,” in 2021 6th International Conference on Inventive Computation Technologies (ICICT). IEEE, 2021, pp. 713–719. A. Soni and A. Rai, “Kidney stone recognition and extraction using directional emboss svm from computed tomography images,” in 2020 Third International Conference on Multimedia Processing, Communication Information Technology (MPCIT), 2020, pp. 57–62..

Plagiarism of survey report:. KLE TECHNOLOGICAL UNIVERSITY Belagavi Campus DR. M. S. SHESHGIRI COLLEGE OF ENGINEERING AND TECHNOLOGY.