Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques

Lakshmana; P. V.Bhaskar Reddy; Megha P. Arakeri

doi:10.1109/ICCAMS60113.2023.10525882

Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques

Lakshmana
, P. V.Bhaskar Reddy
, Megha P. Arakeri^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Accurate diagnosis of liver tumor is important task for the physician. The purpose of this research work is to investigate the utility of feature extraction and classification techniques in the diagnosis of liver tumor CT images as benign(non-cancerous) and malign(cancerous). The classification results can give useful information for the physician in diagnosing the tumor. In this work, the important features representing the tumor are extracted using SURF, LTP, and PCA. Then extracted features are passed to LSTM to identify the type of liver tumor. Experiments are carried out on the dataset collected from hospital. It is observed that the proposed method performs better when compared to the radiologist results.

Original language	English
Title of host publication	2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350317060
DOIs	https://doi.org/10.1109/ICCAMS60113.2023.10525882
Publication status	Published - 2023
Event	2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023 - Bangalore, India Duration: 27-10-2023 → 28-10-2023

Publication series

Name	2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

Conference

Conference	2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023
Country/Territory	India
City	Bangalore
Period	27-10-23 → 28-10-23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Signal Processing
Information Systems and Management
Safety, Risk, Reliability and Quality
Control and Optimization
Business and International Management
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCAMS60113.2023.10525882

Cite this

Lakshmana, Reddy, P. V. B., & Arakeri, M. P. (2023). Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques. In 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023 (2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAMS60113.2023.10525882

Lakshmana ; Reddy, P. V.Bhaskar ; Arakeri, Megha P. / Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques. 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023).

@inproceedings{672b81a3e027446eb72551713b3d7f26,

title = "Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques",

abstract = "Accurate diagnosis of liver tumor is important task for the physician. The purpose of this research work is to investigate the utility of feature extraction and classification techniques in the diagnosis of liver tumor CT images as benign(non-cancerous) and malign(cancerous). The classification results can give useful information for the physician in diagnosing the tumor. In this work, the important features representing the tumor are extracted using SURF, LTP, and PCA. Then extracted features are passed to LSTM to identify the type of liver tumor. Experiments are carried out on the dataset collected from hospital. It is observed that the proposed method performs better when compared to the radiologist results.",

author = "Lakshmana and Reddy, \{P. V.Bhaskar\} and Arakeri, \{Megha P.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023 ; Conference date: 27-10-2023 Through 28-10-2023",

year = "2023",

doi = "10.1109/ICCAMS60113.2023.10525882",

language = "English",

series = "2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023",

address = "United States",

}

Lakshmana, Reddy, PVB & Arakeri, MP 2023, Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques. in 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023. 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023, Bangalore, India, 27-10-23. https://doi.org/10.1109/ICCAMS60113.2023.10525882

Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques. / Lakshmana; Reddy, P. V.Bhaskar; Arakeri, Megha P.
2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques

AU - Lakshmana,

AU - Reddy, P. V.Bhaskar

AU - Arakeri, Megha P.

PY - 2023

Y1 - 2023

N2 - Accurate diagnosis of liver tumor is important task for the physician. The purpose of this research work is to investigate the utility of feature extraction and classification techniques in the diagnosis of liver tumor CT images as benign(non-cancerous) and malign(cancerous). The classification results can give useful information for the physician in diagnosing the tumor. In this work, the important features representing the tumor are extracted using SURF, LTP, and PCA. Then extracted features are passed to LSTM to identify the type of liver tumor. Experiments are carried out on the dataset collected from hospital. It is observed that the proposed method performs better when compared to the radiologist results.

AB - Accurate diagnosis of liver tumor is important task for the physician. The purpose of this research work is to investigate the utility of feature extraction and classification techniques in the diagnosis of liver tumor CT images as benign(non-cancerous) and malign(cancerous). The classification results can give useful information for the physician in diagnosing the tumor. In this work, the important features representing the tumor are extracted using SURF, LTP, and PCA. Then extracted features are passed to LSTM to identify the type of liver tumor. Experiments are carried out on the dataset collected from hospital. It is observed that the proposed method performs better when compared to the radiologist results.

UR - https://www.scopus.com/pages/publications/85194143108

UR - https://www.scopus.com/pages/publications/85194143108#tab=citedBy

U2 - 10.1109/ICCAMS60113.2023.10525882

DO - 10.1109/ICCAMS60113.2023.10525882

M3 - Conference contribution

AN - SCOPUS:85194143108

T3 - 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

BT - 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

Y2 - 27 October 2023 through 28 October 2023

ER -

Lakshmana, Reddy PVB, Arakeri MP. Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques. In 2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023). doi: 10.1109/ICCAMS60113.2023.10525882

Computed Tomography Liver Tumor Image Classification Using Hybrid Feature Extraction and Classification Techniques

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this