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Title: :  MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS
PaperId: :  22779
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22779
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mrs.K.Sandhya RaniVASIREDDY VENKATADRI INSTITUTE OF TECHNOLOGY
JYESTA SAI MANOJVASIREDDY VENKATADRI INSTITUTE OF TECHNOLOGY
Nannebayena Venkata krishnaVASIREDDY VENKATADRI INSTITUTE OF TECHNOLOGY
Kothapalli VijayVASIREDDY VENKATADRI INSTITUTE OF TECHNOLOGY
Ganjikunta Sai KiranVASIREDDY VENKATADRI INSTITUTE OF TECHNOLOGY

Abstract

COMPUTER ENGINEERING
Encoder, Accuracy, Multiclass U-Net, Decoder, Segmentation
Liver tumor segmentation in CT(Computed Tomography) scan images plays a critical role in diagnosis, treatment planning and monitoring of liver diseases. In recent years, deep learning techniques, particularly the U-Net architecture, have shown remarkable success in medical image segmentation tasks. However, segmenting both liver and tumor regions accurately in CT scans presents unique challenges due to variations in shape,size and intensity levels of lesions.This paper presents a novel multiclass U-Net architecture designed specifically for liver tumor segmentation in CT scan images. The proposed model integrates both liver and tumor classes into a unified segmentation framework, enabling simultaneous extraction of relevant anatomical structures and pathological regions. The U-Net architecture is well-suited for this task, as it effectively captures spatial dependencies and hierarchical features within the input images.Key components of the proposed multiclass U-Net include an encoder-decoder structure with skip connections for feature fusion, convolutional layers with batch normalization and non-linear activations, and a final softmax layer for pixel-wise classification into liver and tumor classes. The model is trained using a large dataset of annotated CT scans, leveraging techniques such as data augmentation and transfer learning to improve generalization performance.The proposed multiclass U-Net achieves state-of-the-art performance in liver tumor segmentation tasks, accurately delineating both liver and tumor regions across different patient cohorts.The experiments demonstrated that this method can accurately segment liver tumors.We achieved True value Accuracy of up to 98.4%.

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IJARIIE Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran. "MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 658-665
MLA Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran. "MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 658-665.
APA Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, & Ganjikunta Sai Kiran. (2024). MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 658-665.
Chicago Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran. "MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 658-665.
Oxford Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran. 'MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 658-665. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/MULTICLASS_U_NET_FOR_LIVER_AND_TUMOR_SEGMENTATION_IN_ABDOMEN_CT_SCANS_ijariie22779.pdf (Accessed : ).
Harvard Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran. (2024) 'MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 658-665IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/MULTICLASS_U_NET_FOR_LIVER_AND_TUMOR_SEGMENTATION_IN_ABDOMEN_CT_SCANS_ijariie22779.pdf (Accessed : )
IEEE Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran, "MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 658-665, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/MULTICLASS_U_NET_FOR_LIVER_AND_TUMOR_SEGMENTATION_IN_ABDOMEN_CT_SCANS_ijariie22779.pdf [Accessed : ].
Turabian Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran. "MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver Mrs.K.Sandhya Rani, JYESTA SAI MANOJ, Nannebayena Venkata krishna, Kothapalli Vijay, and Ganjikunta Sai Kiran. MULTICLASS U-NET FOR LIVER AND TUMOR SEGMENTATION IN ABDOMEN CT SCANS. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 658-665. Available from: https://ijariie.com/AdminUploadPdf/MULTICLASS_U_NET_FOR_LIVER_AND_TUMOR_SEGMENTATION_IN_ABDOMEN_CT_SCANS_ijariie22779.pdf
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