Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification
Medical image analysis for pancreatic cancer (PC) classification and recognition is a vital domain of research and medical practices. PC is challenging to diagnose and treat; medical imaging approaches aid early diagnosis to analyse and treat, and employ of medical imaging approaches are support ear...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10287872/ |
| _version_ | 1797649812544290816 |
|---|---|
| author | Janjhyam Venkata Naga Ramesh T. Abirami T. Gopalakrishnan Kanagaraj Narayanasamy Mohamad Khairi Ishak Faten Khalid Karim Samih M. Mostafa Alaa Allakany |
| author_facet | Janjhyam Venkata Naga Ramesh T. Abirami T. Gopalakrishnan Kanagaraj Narayanasamy Mohamad Khairi Ishak Faten Khalid Karim Samih M. Mostafa Alaa Allakany |
| author_sort | Janjhyam Venkata Naga Ramesh |
| collection | DOAJ |
| description | Medical image analysis for pancreatic cancer (PC) classification and recognition is a vital domain of research and medical practices. PC is challenging to diagnose and treat; medical imaging approaches aid early diagnosis to analyse and treat, and employ of medical imaging approaches are support early diagnosis, correct analysis, and treatment planning. Computed Tomography (CT) scans are generally utilized to detect and classify PCs. Deep learning (DL) approaches have demonstrated the ability to support the diagnosis and detection of several medical conditions, containing PC. Convolutional Neural Networks (CNNs) are a kind of DL approach generally employed for image analysis that is trained to automatically learn and extract features in medical images. So, this study purposes a new Sparrow Search Algorithm with Stacked Deep Learning based Medical Image Analysis for Pancreatic Cancer Detection and Classification (SSASDL-PCDC) technique on CT images. The purpose of the study is to design an SSASDL-PCDC technique to achieve improved pancreatic cancer detection performance. In addition, the SSASDL-PCDC technique applies Harris Hawks Optimization (HHO) with a densely connected networks (DenseNet) model for the feature extraction process. Moreover, convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM) approach was utilized for PC detection and classification. Furthermore, Sparrow Search Algorithm (SSA) is used to adjust the hyperparameter values of the CNN-BiLSTM technique. To evaluate the effectiveness of the SSASDL-PCDC technique, extensive experiments were executed on a comprehensive database of pancreatic CT images. The simulation outcome value depicted that the SSASDL-PCDC technique with maximum sensitivity of 99.26%, specificity of 99.26%, and accuracy of 99.26%. |
| first_indexed | 2024-03-11T15:51:24Z |
| format | Article |
| id | doaj.art-e5d0e47b7b0e46f0a8c7d765c327e578 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T15:51:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e5d0e47b7b0e46f0a8c7d765c327e5782023-10-25T23:01:24ZengIEEEIEEE Access2169-35362023-01-011111192711193510.1109/ACCESS.2023.332237610287872Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and ClassificationJanjhyam Venkata Naga Ramesh0https://orcid.org/0000-0002-2084-8864T. Abirami1T. Gopalakrishnan2https://orcid.org/0000-0002-3124-322XKanagaraj Narayanasamy3Mohamad Khairi Ishak4https://orcid.org/0000-0002-3554-0061Faten Khalid Karim5https://orcid.org/0000-0003-1111-5818Samih M. Mostafa6https://orcid.org/0000-0001-9234-5898Alaa Allakany7https://orcid.org/0000-0001-6451-5407Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, Andhra Pradesh, IndiaDepartment of Information Technology, Kongu Engineering College, Erode, IndiaDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Computer Science, Karpagam Academy of Higher Education, Coimbatore, IndiaSchool of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, MalaysiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaComputer Science Department, Faculty of Computers and Information, South Valley University, Qena, EgyptFaculty of Computers and Information, Kafrelsheikh University, Kafr El-Shaikh, EgyptMedical image analysis for pancreatic cancer (PC) classification and recognition is a vital domain of research and medical practices. PC is challenging to diagnose and treat; medical imaging approaches aid early diagnosis to analyse and treat, and employ of medical imaging approaches are support early diagnosis, correct analysis, and treatment planning. Computed Tomography (CT) scans are generally utilized to detect and classify PCs. Deep learning (DL) approaches have demonstrated the ability to support the diagnosis and detection of several medical conditions, containing PC. Convolutional Neural Networks (CNNs) are a kind of DL approach generally employed for image analysis that is trained to automatically learn and extract features in medical images. So, this study purposes a new Sparrow Search Algorithm with Stacked Deep Learning based Medical Image Analysis for Pancreatic Cancer Detection and Classification (SSASDL-PCDC) technique on CT images. The purpose of the study is to design an SSASDL-PCDC technique to achieve improved pancreatic cancer detection performance. In addition, the SSASDL-PCDC technique applies Harris Hawks Optimization (HHO) with a densely connected networks (DenseNet) model for the feature extraction process. Moreover, convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM) approach was utilized for PC detection and classification. Furthermore, Sparrow Search Algorithm (SSA) is used to adjust the hyperparameter values of the CNN-BiLSTM technique. To evaluate the effectiveness of the SSASDL-PCDC technique, extensive experiments were executed on a comprehensive database of pancreatic CT images. The simulation outcome value depicted that the SSASDL-PCDC technique with maximum sensitivity of 99.26%, specificity of 99.26%, and accuracy of 99.26%.https://ieeexplore.ieee.org/document/10287872/Pancreatic cancercomputed tomography imagessparrow search algorithmmedical image analysiscancer diagnosis |
| spellingShingle | Janjhyam Venkata Naga Ramesh T. Abirami T. Gopalakrishnan Kanagaraj Narayanasamy Mohamad Khairi Ishak Faten Khalid Karim Samih M. Mostafa Alaa Allakany Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification IEEE Access Pancreatic cancer computed tomography images sparrow search algorithm medical image analysis cancer diagnosis |
| title | Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification |
| title_full | Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification |
| title_fullStr | Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification |
| title_full_unstemmed | Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification |
| title_short | Sparrow Search Algorithm With Stacked Deep Learning Based Medical Image Analysis for Pancreatic Cancer Detection and Classification |
| title_sort | sparrow search algorithm with stacked deep learning based medical image analysis for pancreatic cancer detection and classification |
| topic | Pancreatic cancer computed tomography images sparrow search algorithm medical image analysis cancer diagnosis |
| url | https://ieeexplore.ieee.org/document/10287872/ |
| work_keys_str_mv | AT janjhyamvenkatanagaramesh sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification AT tabirami sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification AT tgopalakrishnan sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification AT kanagarajnarayanasamy sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification AT mohamadkhairiishak sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification AT fatenkhalidkarim sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification AT samihmmostafa sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification AT alaaallakany sparrowsearchalgorithmwithstackeddeeplearningbasedmedicalimageanalysisforpancreaticcancerdetectionandclassification |