A modified deep convolutional neural network for abnormal brain image classification

Hemanth, D. Jude; Anitha, J.; Naaji, Antoanela; Geman, Oana; Popescu, Daniela Elena; Hoang Son, Le

doi:10.1109/access.2018.2885639

Cited by 164Brain Tumor Detection and ClassificationAdvanced Neural Network ApplicationsAI in cancer detection

A modified deep convolutional neural network for abnormal brain image classification

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Hemanth, D. Jude, Anitha, J., Naaji, Antoanela, Geman, Oana, Popescu, Daniela Elena, Hoang Son, Le·2019·IEEE Access

DOI 10.1109/access.2018.2885639

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Abstract

Deep learning techniques have gained significant importance among artificial intelligence techniques for any computing applications. Among them, deep convolutional neural networks (DCNNs) is one of the widely used deep learning networks for any practical applications. The accuracy is generally high and the manual feature extraction process is not necessary in these networks. However, the high accuracy is achieved at the cost of huge computational complexity. The complexity in DCNN is mainly due to: 1) increased number of layers between input and output layers and 2) two set of parameters (one set of filter coefficients and another set of weights) in the fully connected network need to be adjusted. In this paper, the second aspect is targeted to reduce the computational complexity of conventional DCNN. Suitable modifications are performed in the training algorithm to reduce the number of parameter adjustments. The weight adjustment process in the fully connected layer is completely eliminated in the proposed modified approach. Instead, a simple assignment process is used to find the weights of this fully connected layer. Thus, the computational complexity is significantly reduced in the proposed approach. The application of modified DCNN is explored in the context of magnetic resonance brain tumor image classification. Abnormal brain tumor images from four different classes are used in this paper. The experimental results show promising results for the proposed approach.

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End-to-end convolutional neural networks have dominated performance at diverse medical imaging tasks.
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Convolutional neural networks have been used successfully in medical imaging applications including mammography, brain imaging, and prostate cancer detection.
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From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [16]

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Siddique, A. A., Raza, A., Alshehri, M. S., Alasbali, N. A., & Abbasi, S. F. (2024) · IEEE Access · doi:10.1109/access.2024.3412412 · Cited by 22 · see citing works
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Ali, S. S. A., Memon, K., Yahya, N., & Khan, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11146-5 · Cited by 24 · see citing works
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Subbarao, M. V., Kumar, T. S., Chowdary, P. S. R., & Chakravarthy, V. V. S. S. S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1559-8_41 · Cited by 17 · see citing works
A comparative analysis and classification of cancerous brain tumors detection based on classical machine learning and deep transfer learning models
Singh, Y. P., & Lobiyal, D. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16637-7 · Cited by 17 · see citing works
GraphMriNet: a few-shot brain tumor MRI image classification model based on Prewitt operator and graph isomorphic network
Liao, B., Zuo, H., Yu, Y., & Li, Y. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01530-z · Cited by 17 · see citing works
An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data
Roy, P., Srijon, F. M. S., & Bhowmik, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310748 · Cited by 16 · see citing works
Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis
Dewage, K. A. K. W., Hasan, R., Rehman, B., & Mahmood, S. (2024) · Information · doi:10.3390/info15100653 · Cited by 16 · see citing works
Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques
Henry, A. G. P., & Anitha, J. (2021) · Open Computer Science · doi:10.1515/comp-2020-0177 · Cited by 15 · see citing works
Hybrid Deep Learning Models for Multi-classification of Tumour from Brain MRI
Munira, H. A., & Islam, M. S. (2022) · Journal of Information Systems Engineering and Business Intelligence · doi:10.20473/jisebi.8.2.162-174 · Cited by 14 · see citing works
Brain tumour classification: a comprehensive systematic review on various constraints
Chaithanyadas, K. V., & King, G. R. G. (2022) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2022.2083019 · Cited by 13 · see citing works
LSTM-ESA HİBRİT MODELİ İLE MR GÖRÜNTÜLERİNDEN BEYİN TÜMÖRÜNÜN SINIFLANDIRILMASI
Aslan, E. (2024) · Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.54365/adyumbd.1391157 · Cited by 13 · see citing works
A novel multi-class brain tumor classification method based on unsupervised PCANet features
Shahin, A. I., Aly, S., & Aly, W. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08281-x · Cited by 13 · see citing works
Adaptive convolutional neural network using N-gram for spatial object recognition
Bapu, J. J., Florinabel, D. J., Robinson, Y. H., Julie, E., Kumar, R., Vo, N. T. N., Son, L.-H.-A., Trần, T. M., & Giap, C. N. (2019) · Earth Science Informatics · doi:10.1007/s12145-019-00396-x · Cited by 11 · see citing works
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Jasim, H. M., Ghrabat, M. J. J., Abdulrahman, L. Q., Nyangaresi, V. O., Ma, J., Abduljabbar, Z. A., & Abduljaleel, I. Q. (2023) · Informatica · doi:10.31449/inf.v47i8.4840 · Cited by 12 · see citing works
An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks
Han, T. T., Van, H. N., Thanh, H. N. T., Vu, T. A., Tuan, D. N., & Vu, D. L. (2022) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2022/2092985 · Cited by 12 · see citing works
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Chauhan, N., & Choi, B.-J. (2019) · International Journal of Fuzzy Logic and Intelligent Systems · doi:10.5391/ijfis.2019.19.4.315 · Cited by 12 · see citing works
A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI
Ahmed, M., Khalifa, F., Moustafa, H. E.-D., Saleh, G. A., & AbdElhalim, E. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i2.pp1192-1202 · Cited by 11 · see citing works
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Manjunath, R. V., Yashaswini Gowda, N., Manu, H. M., & Lutimath, N. M. (2024) · Research Square · doi:10.21203/rs.3.rs-3998886/v1 · Cited by 11 · see citing works
Neutrosophic approach for enhancing quality of signals
Jha, S., Kumar, R., Sơn, L. H., Chiclana, F., Puri, V., & Priyadarshini, I. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7375-0 · Cited by 10 · see citing works
A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination
Narayanan, K. L., Krishnan, R. S., Julie, E. G., Robinson, H., Kumar, R., Sơn, L. H., Hung, T. X., Samui, P., Ngo, P. T. T., & Bui, D. T. (2020) · Applied Sciences · doi:10.3390/app10020718 · Cited by 10 · see citing works
Ensemble Transfer Learning for Brain Tumor Classification
Remzan, N., Tahiry, K., & Farchi, A. (2022) · International Conference on Advanced Communication Technologies and Networking (CommNet) · doi:10.1109/commnet56067.2022.9993831 · Cited by 10 · see citing works
Brain Tumor Classification using Ensemble Classifiers
Subbarao, M. V., Ram, G. C., Kumar, D. G., & Terlapu, S. K. (2022) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears53579.2022.9752177 · Cited by 9 · see citing works
Performance enhanced ripplet transform based compression method for medical images
Anitha, J., Sophia, P. E., Sơn, L. H., & de Albuquerque, V. H. C. (2019) · Measurement · doi:10.1016/j.measurement.2019.04.036 · Cited by 9 · see citing works
Combined CNN and Pixel Feature Image for Fatty Liver Ultrasound Image Classification
Zhu, H., Liu, Y., Gao, X., & Zhang, L. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/9385734 · Cited by 8 · see citing works
Performance Analysis of Machine Learning Techniques for Multi-Organ Cancer Detection and Classification: A Comparative Study
Priya, M. L. V. A., & Subbarao, M. V. (2023) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434014 · Cited by 8 · see citing works
Brain tumor classification on MRI images using fine-tuned deep learning models
Manjunath, R. V., Yashaswini Gowda, N., Manu, H. M., Bharath, K. N., & Manasa, R. (2025) · Machine learning for computational science and engineering · doi:10.1007/s44379-025-00024-0 · Cited by 7 · see citing works
Smart architectures: computerized classification of brain tumors from MRI images utilizing deep learning approaches
Mijwil, M. M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20349-x · Cited by 7 · see citing works
Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression
Qin, H., Hussain, L., Liu, Z., Yan, X., Awwad, F. A. A., Butt, F. M., Salaria, U. A., & Ismail, E. A. A. (2025) · Digital Health · doi:10.1177/20552076251349719 · Cited by 7 · see citing works
Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images
Yılmaz, V. S., Akdag, M., Dalveren, Y., Doruk, R. Ö., Kara, A., & Soylu, A. (2023) · Diagnostics · doi:10.3390/diagnostics13040651 · Cited by 7 · see citing works
Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology
Pitarch, C., Ungan, G., Julià‐Sapé, M., & Vellido, A. (2024) · Cancers · doi:10.3390/cancers16020300 · Cited by 7 · see citing works
Multi‐class brain tumor classification system in MRI images using cascades neural network
Jayachandran, A., & Anisha, N. (2024) · Computational Intelligence · doi:10.1111/coin.12687 · Cited by 6 · see citing works
Medical Image-based Prediction of Brain Tumor by Using Convolutional Neural Network Optimized by Cuckoo Search Algorithm
Azizy, F. M., Jondri, J., & Kurniawan, I. (2023) · doi:10.1109/icoict58202.2023.10262447 · Cited by 6 · see citing works
An Intelligent Smart Dynamic Feature Analysis Based Approach by Utilizing Deep Learning to Improve the Breast Cancer Detection
Shim, S., Hussain, L., Alsolami, E., & Alkinani, M. H. (2025) · IEEE Access · doi:10.1109/access.2025.3566353 · Cited by 5 · see citing works
Deep Learning CNN Model-Based Anomaly Detection in 3D Brain MRI Images using Feature Distribution Similarity
Panyala, A. R., & Baskar, M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140330 · Cited by 5 · see citing works
Explainable Artificial Intelligence for brain Tumor Detection
Kaushal, S., Sonali, S., Gaur, H., Bhati, G., & Rai, A. K. (2024) · International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) · doi:10.1109/icaiccit64383.2024.10912262 · Cited by 5 · see citing works
Recalling‐enhanced recurrent neural network optimized with wood pecker mating algorithm for brain tumor classification
Suganthy, M., Ashok, S., Maheswari, A. U., & Subha, T. D. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7729 · Cited by 5 · see citing works
AVRM: adaptive void recovery mechanism to reduce void nodes in wireless sensor networks
Ayyasamy, A., Julie, E. G., Robinson, H., Balaji, S., Kumar, R., Sơn, L. H., Thong, P. H., & Priyadarshini, I. (2020) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-019-00865-6 · Cited by 5 · see citing works

A Modified Deep Convolutional Neural Network for Brian Abnormalities DetectionPreprint
Perumprath, A., Arun, R., & Raj, V. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3913583
A Deep Learning Based Convolution Neural Network-DCNN Approach to Detect Brain Tumor
Shrestha, H., Dhasarathan, C., Kumar, M., Nidhya, R., Shankar, A., & Kumar, M. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-6887-6_11 · Cited by 16 · see citing works
Deep Convolutional Neural Network-based Automatic Detection of Brain Tumour
Paul, I., Sahu, A., Das, P. K., & Meher, S. (2023) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon57975.2023.10101238 · Cited by 16 · see citing works
Performance Optimization of Brain Tumor Detection and Classification Based MRI by Using Batch Normalization Algorithms in Deep Convolution Neural Network
Tin, T. A., Aye, M. M., Khin, E. E., Oo, T., Tun, H. M., & Pradhan, D. (2024) · Journal of Novel Engineering Science and Technology · doi:10.56741/jnest.v3i03.567
Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function
Huang, Z., Du, X., Chen, L., Li, Y., Liu, M., Chou, Y., & Jin, L. (2020) · IEEE Access · doi:10.1109/access.2020.2993618 · Cited by 159 · see citing works

Authors: Hemanth, D. J., Anitha, J., Naaji, A., Geman, O., Popescu, D. E., & Hoang Son, L.
Affiliations: Karunya University (IN)
Vasile Goldis Western University of Arad (RO)
Ştefan cel Mare University of Suceava (RO)
University of Oradea (RO)
Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
ORCID: Hemanth, D. Jude
Anitha, J.
Naaji, Antoanela
Geman, Oana
Popescu, Daniela Elena
Hoang Son, Le
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019
Volume: 7
Pages: 4275-4283
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2018.2885639
OpenAlex: W2904910159
Cited by: 164
References: 20
Citations by year: 2026: 7 · 2025: 14 · 2024: 28 · 2023: 40 · 2022: 31 · 2021: 15 · 2020: 9 · 2019: 20
FWCI: 4.96
Type: journal article
Language: en
Open access: gold
doi.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: IEEE Access (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords: brain tumor classification, magnetic resonance imaging, computational complexity reduction, weight assignment, fully connected layers

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A modified deep convolutional neural network for abnormal brain image classification

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