A DenseNet CNN-based liver lesion prediction and classification for future medical diagnosis

被引:15
作者
Prakash, N. Nanda [1 ]
Rajesh, V [1 ]
Namakhwa, Dumisani Lickson [3 ,4 ]
Pande, Sandeep Dwarkanath [2 ]
Ahammad, Sk Hasane [1 ]
机构
[1] Koneru Lakshmaiah Educ Fdn, Dept ECE, Vaddeswaram 522302, India
[2] MIT, Acad Engn, Pune, India
[3] Appl Sci Private Univ, Appl Sci Res Ctr, Amman, Jordan
[4] Malawi Univ Sci & Technol, Appl Sci, Blantyre, Malawi
关键词
DenseNet CNN; Liver lesion detection; Segmentation; Classification; NEURAL-NETWORK;
D O I
10.1016/j.sciaf.2023.e01629
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Liver disease diagnosis is a major medical challenge in developing nations. Every year around 30 billion people face liver failure issues resulting in their death. The past liver ab-normality detection models have faced less accuracy and high theory of constraint metrics. The lesion on the liver hasn't been identified clearly with earlier models, so an advanced, efficient, and effective liver disease detection is essential. To overcome the limitations of existing models, this approach proposes a deep liver abnormality detection with DenseNet convolutional neural network (CNN) based deep learning technique. This work collected liver Computed Tomography (CT) scan images from Kaggle dataset for training in the ini-tial stage. The pre-processing has been performed with region-growing segmentation, and training is performed through DenseNet CNN. The real-time test images are collected from Government General Hospital Vijayawada (10,0 0 0 samples), verified on proposed DenseNet CNN to diagnose whether the input has a liver lesion. Finally, the results obtained and de-rived confusion matrix summarizes the performance of the proposed methodology with following metrics of accuracy at 98.34%, sensitivity at 99.72%, recall at 97.84%, throughput at 98.43% and detection rate at 93.41%. The comparison results reveals that the proposed technique attains more accuracy and outperforms the other pioneer methodologies.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
引用
收藏
页数:16
相关论文
共 50 条
[41]   Minimalistic CNN-based ensemble model for gender prediction from face images [J].
Antipov, Grigory ;
Berrani, Sid-Ahmed ;
Dugelay, Jean-Luc .
PATTERN RECOGNITION LETTERS, 2016, 70 :59-65
[42]   Texture Feature based Liver Lesion Classification [J].
Doron, Ye'ela ;
Mayer-Wolf, Nitzan ;
Diamant, Idit ;
Greenspan, Hayit .
MEDICAL IMAGING 2014: COMPUTER-AIDED DIAGNOSIS, 2014, 9035
[43]   Fully-Automated CNN-Based Computer Aided Celiac Disease Diagnosis [J].
Gadermayr, Michael ;
Wimmer, Georg ;
Uhl, Andreas ;
Kogler, Hubert ;
Vecsei, Andreas ;
Merhof, Dorit .
IMAGE ANALYSIS AND PROCESSING (ICIAP 2017), PT II, 2017, 10485 :467-478
[44]   Enhanced Segmentation with Optimized Nine-Layered CNN-Based Classification of Leaf Diseases: An Automatic Approach for Plant Disease Diagnosis [J].
Chillakuru, Prameeladevi ;
Divya, D. ;
Ananthajothi, K. .
CYBERNETICS AND SYSTEMS, 2024, 55 (08) :1867-1902
[45]   CNN-based dermoscopic analysis of vascular skin lesions in the prognosis of skin lesion sarcoma based on ensemble learning [J].
Muthulakshmi, V. ;
Hemapriya, N. .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 45 (06) :11515-11534
[46]   Diabetic retinopathy detection using EADBSC and improved dilated ensemble CNN-based classification [J].
Thomas, Neetha Merin ;
Jerome, S. Albert .
MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (11) :33573-33595
[47]   Lightweight CNN-based seizure classification via leveraging chimera states in iEEG recordings [J].
Azad, Fatemeh ;
Shouraki, Saeed Bagheri ;
Nazari, Soheila ;
Chan, Mansun .
RESULTS IN ENGINEERING, 2025, 27
[48]   Deep learning to combat knee osteoarthritis and severity assessment by using CNN-based classification [J].
Rani, Suman ;
Memoria, Minakshi ;
Almogren, Ahmad ;
Bharany, Salil ;
Joshi, Kapil ;
Altameem, Ayman ;
Rehman, Ateeq Ur ;
Hamam, Habib .
BMC MUSCULOSKELETAL DISORDERS, 2024, 25 (01)
[49]   CNN-based fault classification using combination image of feature vectors in rotor systems [J].
Min, Tae Hong ;
Lee, Jeong Jun ;
Cheong, Deok Young ;
Choi, Byeong Keun ;
Park, Dong Hee .
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2024, 38 (11) :5829-5839
[50]   CNN-Based Classification of Illustrator Style in Graphic Novels: Which Features Contribute Most? [J].
Laubrock, Jochen ;
Dubray, David .
MULTIMEDIA MODELING, MMM 2019, PT II, 2019, 11296 :684-695