American Journal of Biomedical Engineering
p-ISSN: 2163-1050 e-ISSN: 2163-1077
2022; 11(1): 1-12
doi:10.5923/j.ajbe.20221101.01
Received: Mar. 8, 2022; Accepted: Mar. 21, 2022; Published: Apr. 22, 2022
Ihab S. Atta 1, 2, Ashraf S. Emam 3, Ali H. Al-Boghdady 4, Saeed M. Badran 5, Mohamed F. El-Refaei 6, Ossama B. Abouelatta 7
1Department of Pathology, Faculty of Medicine, Al-Baha University, Al-Baha, KSA
2Department of Pathology, Faculty of Medicine, Al-Azhar University, Cairo, Egypt
3Department of Automotive and Tractors Engineering, Faculty of Engineering, Mataria, Helwan University, Cairo, Egypt
4Department of Mechanical Engineering, Faculty of Engineering, Al-Baha University, Al-Baha, KSA
5Department of Electrical Engineering, Faculty of Engineering, Al-Baha University, Al-Baha, KSA
6Department of Biochemistry, Faculty of Medicine, Al-Baha University, Al-Baha, KSA
7Department of Production Engineering & Mechanical Design, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Correspondence to: Ossama B. Abouelatta , Department of Production Engineering & Mechanical Design, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
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Copyright © 2022 The Author(s). Published by Scientific & Academic Publishing.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Traditional histopathology examination remains a serious task in cancer identification and is clinically vital to division the cancer tissues and group them into numerous classes. However, the diagnostic process is subjective, and the variations among technical observers and time consumed are considerable. Reliable, automated cancer detection assistance is currently an increasingly important task in the medical field. This study aims to classify different cancer tumor types. A comprehensive analysis of a new classification technique based on image processing and composition properties was performed. A graphical user interface (GUI) program dedicated to the classification and identification of cancer cell images was developed and created in-house using Matlab package. As a result, the data can improve the diagnostic capabilities of physicians and reduce the time required for precise diagnosis. The average discrimination rate demonstrates the validity of the proposed technique in distinguishing between benign and malignant lesions. This simple procedure is an encouraging application of digital image processing performance in the histopathology field compared with traditional methods. Further investigations in the future may demonstrate a great advantage in the prediction and classification of cell morphology and cancer grading using the computed segmentation technique.
Keywords: Cancer, Histopathology, Automatic classification, Image processing, Texture features, Neural network
Cite this paper: Ihab S. Atta , Ashraf S. Emam , Ali H. Al-Boghdady , Saeed M. Badran , Mohamed F. El-Refaei , Ossama B. Abouelatta , Assessment of a Neural Network Based on Texture Features Analysis: The Impact of Classifying Cancer Types Using Image Processing, American Journal of Biomedical Engineering, Vol. 11 No. 1, 2022, pp. 1-12. doi: 10.5923/j.ajbe.20221101.01.
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Figure 1. Basic knowledge of ANN |
Figure 2. Block diagram of the procedure |
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Figure 3. The main interface of Automatic Classification of Tissue Morphology, ACoTM |
Figure 7. (a): A case of skin biopsy showed features of squamous cell carcinoma (×200), (b): A case of basal cell carcinoma showed basaloid features with palisading towards the periphery (×400) |
Figure 8. (a): A case of skin biopsy showed features of squamous cell carcinoma (×200), (b): A case of basal cell carcinoma showed basaloid features with palisading towards the periphery (×400) |
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Figure 9. Percentage success in classification for colon, prostate, testicles, skin, and uterine |
Figure 10. Percentage success in classification for benign and malignant colon, prostate, testicles, skin, and uterine |
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