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International Journal of Neural Network, 2022, 3(3); doi: 10.38007/NN.2022.030305.

Image Content Segmentation Based on Convolutional Neural Network


Ernest Dadparvar

Corresponding Author:
Ernest Dadparvar

Jimma University, Ethiopia


In real life, images are widely used in medicine, commercial marketing and other fields. Image recognition technology has developed rapidly and has played a huge role in our daily life and work. So in order to make better use of image technology, this paper intends to delve into image content segmentation in convolutional neural networks. This paper mainly studies the role of convolutional neural networks and image segmentation in specific operations through experimental methods and interactive ratio IoU analysis methods. The experimental data show that in the PASCAL VOC 2015 and IRCAD datasets, the optimal parameters obtained by G-FCN are α=0.6, β=0.6. Therefore, the result of using the improved algorithm is close to the marked result, which can better reflect the details of the image, and the segmentation effect is better.


Convolutional Neural Network, Image Content Segmentation, Segmentation Technology, Application Method

Cite This Paper

Ernest Dadparvar. Image Content Segmentation Based on Convolutional Neural Network. International Journal of Neural Network (2022), Vol. 3, Issue 3: 36-44. https://doi.org/10.38007/NN.2022.030305.


[1] Madhu, Raman Kumar: A Hybrid Feature Extraction Technique for Content Based Medical Image Retrieval Using Segmentation and Clustering Techniques. Multim. Tools Appl. 81(6): 8871-8904 (2022). https://doi.org/10.1007/s11042-022-11901-8

[2] Lakshmana, Sunil Kumar S. Manvi, K. G. Karibasappa: Hybrid Kernel Fuzzy C-means Clustering Segmentation Algorithm for Content Based Medical Image Retrieval Application. Int. J. Bioinform. Res. Appl. 17(6): 496-511 (2021). https://doi.org/10.1504/IJBRA.2021.120534

[3] Sneha Kugunavar, C. J. Prabhakar: Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique. J. Inf. Technol. Res. 14(2): 48-66 (2021). https://doi.org/10.4018/JITR.2021040103

[4] Yashwant Kurmi, Vijayshri Chaurasia: Content-Based Image Retrieval Algorithm for Nuclei Segmentation in Histopathology Images. Multim. Tools Appl. 80(2): 3017-3037 (2021). https://doi.org/10.1007/s11042-020-09797-3

[5] Hager Merdassi, Walid Barhoumi, Ezzeddine Zagrouba: Optimisation of Linear Dependence Energy for Object Co-Segmentation in a Set of Images with Heterogeneous Contents. IET Image Process. 14(1): 201-210 (2020). https://doi.org/10.1049/iet-ipr.2018.5176

[6] Anastasia Iskhakova, Daniyar Volf, Roman V. Meshcheryakov: Method for Reducing the Feature Space Dimension in Speech Emotion Recognition Using Convolutional Neural Networks. Autom. Remote. Control. 83(6): 857-868 (2022). https://doi.org/10.1134/S0005117922060042

[7] Myasar Mundher Adnan, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Tanzila Saba, Suliman Mohamed Fati, Saeed Ali Bahaj: An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform. IEEE Access 10: 7520-7532 (2022). https://doi.org/10.1109/ACCESS.2022.3140861

[8] Sonam Aggarwal, Sheifali Gupta, Ramani Kannan, Rakesh Ahuja, Deepali Gupta, Sapna Juneja, Samir Brahim Belhaouari: A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images. IEEE Access 10: 83591-83611 (2022). https://doi.org/10.1109/ACCESS.2022.3197189

[9] Michael Opoku Agyeman, Andres Felipe Guerrero, Quoc-Tuan Vien: Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices. IEEE Access 10: 87387-87403 (2022). https://doi.org/10.1109/ACCESS.2022.3192390

[10] Adal A. Alashban, Al-Hanouf Al-Aljmi, Norah F. Alhussainan, Ridha Ouni: Single Convolutional Neural Network With Three Layers Model for Crowd Density Estimation. IEEE Access 10: 63823-63833 (2022). https://doi.org/10.1109/ACCESS.2022.3180738

[11] Eoin Brophy, Bryan M. Hennelly, Maarten De Vos, Geraldine B. Boylan, Tomás Ward: Improved Electrode Motion Artefact Denoising in ECG Using Convolutional Neural Networks and a Custom Loss Function. IEEE Access 10: 54891-54898 (2022). https://doi.org/10.1109/ACCESS.2022.3176971

[12] Asghar Ali Chandio, Md. Asikuzzaman, Mark R. Pickering, Mehjabeen Leghari: Cursive Text Recognition in Natural Scene Images Using Deep Convolutional Recurrent Neural Network. IEEE Access 10: 10062-10078 (2022). https://doi.org/10.1109/ACCESS.2022.3144844

[13] Suci Dwijayanti, Muhammad Iqbal, Bhakti Yudho Suprapto: Real-Time Implementation of Face Recognition and Emotion Recognition in a Humanoid Robot Using a Convolutional Neural Network. IEEE Access 10: 89876-89886 (2022). https://doi.org/10.1109/ACCESS.2022.3200762

[14] Ali Pourramezan Fard, Joe Ferrantelli, Anne-Lise Dupuis, Mohammad H. Mahoor: Sagittal Cervical Spine Landmark Point Detection in X-Ray Using Deep Convolutional Neural Networks. IEEE Access 10: 59413-59427 (2022). https://doi.org/10.1109/ACCESS.2022.3180028

[15] Ahmed Y. Hatata, Mohammed Abd-Elnaby, Bishoy E. Sedhom: Adaptive Protection Scheme for FREEDM Microgrid Based on Convolutional Neural Network and Gorilla Troops Optimization Technique. IEEE Access 10: 55583-55601 (2022). https://doi.org/10.1109/ACCESS.2022.3177544

[16] Svetlana Illarionova, Dmitrii Shadrin, Vladimir Ignatiev, Sergey Shayakhmetov, Alexey Trekin, Ivan V. Oseledets: Estimation of the Canopy Height Model From Multispectral Satellite Imagery With Convolutional Neural Networks. IEEE Access 10: 34116-34132 (2022). https://doi.org/10.1109/ACCESS.2022.3161568

[17] Andac Imak, Adalet Celebi, Kamran Siddique, Muammer Turkoglu, Abdulkadir Sengür, Iftekhar Salam: Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network. IEEE Access 10: 18320-18329 (2022). https://doi.org/10.1109/ACCESS.2022.3150358

[18] Ahmed H. Janabi, Triantafyllos Kanakis, Mark Johnson: Convolutional Neural Network Based Algorithm for Early Warning Proactive System Security in Software Defined Networks. IEEE Access 10: 14301-14310 (2022). https://doi.org/10.1109/ACCESS.2022.3148134