Volume 25 , Issue 1 , PP: 64-74, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Imène Issaoui 1 * , Afef Selmi 2
Doi: https://doi.org/10.54216/IJNS.250105
COVID19 otherwise called Severe Acute Respiratory Syndrome Corona virus-2 is an infectious illness. Another transmittable infection called Pneumonia is mainly attributable to infection because of bacteria in the alveoli of the lungs. Once a diseased lung tissue has infection, it elevates excretion in it. Specialists conduct health examinations and identify the patient through ultrasound, biopsy, or Chest X-ray of lungs to identify whether the patient has these diseases. Incorrect treatment, misdiagnosis, and if the disease was disregarded will result in the fatality. The development of Deep Learning and neutrosophic set (NS) supports the decision-making procedure of professionals to identify patients with this disease. NS is a prolongation of the fuzzy set and classical theories. The NS determines three memberships such as T, I and F. T, I, and F display the degree of truth, the false, and the indeterminacy membership, correspondingly. This enables a more nuanced representation of contradiction, uncertainty, and ambiguity within the dataset, allowing superior handling of imprecise and complex data. This study develops a new Deep learning with Neutrosophic Set-Based k-Nearest Neighbors Classifier for disease detection (DLNSKNN-DD) technique. The major purpose of the DLNSKNN-DD method is to identify the existence of virus pneumonia and COVID-19. In the DLNSKNN-DD technique, the feature extraction from the medical images is carried out by residual network (ResNet50v2). Moreover, the parameter tuning of the ResNetv2 model is done using Adadelta optimizer. The DLNSKNN-DD technique exploits NSKNN model for classification purposes. The performance evaluation of the DLNSKNN-DD algorithm can be assessed on medicinal image dataset. The experimental outcomes underlined the effectual recognition results of the DLNSKNN-DD technique on the identification of diseases
Virus Pneumonia , Sars-Cov-2 , COVID-19 diagnosis , Machine Learning , Neutrosophic Logic , Neutrosophic Soft Set
[1] Abobala, M., "AH-Subspaces in Neutrosophic Vector Spaces", International Journal of Neutrosophic Science, Vol. 6, pp. 80-86, 2020.
[2] Abobala, M., "A Study of AH-Substructures in n-Refined Neutrosophic Vector Spaces", International Journal of Neutrosophic Science", Vol. 9, pp.74-85, 2020.
[3] Khalid, M., Khalid, N.A. and Iqbal, R., 2020. MBJ-neutrosophic T-ideal on B-algebra. International Journal of Neutrosophic Science, 1(1), pp.29-39.
[4] Gamboa-Cruzado, J., Morante-Palomino, E., Rivero, C.A., Bendezú, M.L. and Fernández, D.M.M., 2024. Research on the Classification and Application of Physical Education Teaching Mode by Neutrosophic Analytic Hierarchy Process. International Journal of Neutrosophic Science, 23(3), pp.51-1.
[5] Mathew, L.P., Sebastian, L. and Thankachan, B., 2024. Some Operations on Trapezoidal Single Valued Neutrosophic Fuzzy Numbers. International Journal of Neutrosophic Science, 23(3), pp.29-9.
[6] Goyal, S. and Singh, R., 2023. Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing, 14(4), pp.3239-3259.
[7] Sanghvi, H.A., Patel, R.H., Agarwal, A., Gupta, S., Sawhney, V. and Pandya, A.S., 2023. A deep learning approach for classification of COVID and pneumonia using DenseNet‐201. International Journal of Imaging Systems and Technology, 33(1), pp.18-38.
[8] Ali, A.M., Ghafoor, K., Mulahuwaish, A. and Maghdid, H., 2024. COVID-19 pneumonia level detection using deep learning algorithm and transfer learning. Evolutionary Intelligence, 17(2), pp.1035-1046.
[9] Mohan, G., Subashini, M.M., Balan, S. and Singh, S., 2024. A multiclass deep learning algorithm for healthy lung, Covid-19 and pneumonia disease detection from chest X-ray images. Discover Artificial Intelligence, 4(1), p.20.
[10] Mehmood, M.H., Hassan, F., Rahman, A.U., Khan, W., Mostafa, S.M., Ghadi, Y.Y., Algarni, A. and Ali, M., 2024. CP_DeepNet: a novel automated system for COVID-19 and pneumonia detection through lung X-rays. Multimedia Tools and Applications, pp.1-18.
[11] Ibrahim, D.M., Elshennawy, N.M. and Sarhan, A.M., 2021. Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in biology and medicine, 132, p.104348.
[12] Albahli, S. and Nazir, T., 2023. A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images. Bioengineering, 10(10), p.1147.
[13] Alhasson, H.F., Almozainy, E., Alharbi, M., Almansour, N., Alharbi, S.S. and Khan, R.U., 2023. A Deep Learning-Based Mobile Application for Monkeypox Detection. Applied Sciences, 13(23), p.12589.
[14] Wirtz, A., Mirashi, S.G. and Wesarg, S., 2018. Automatic teeth segmentation in panoramic X-ray images using a coupled shape model in combination with a neural network. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV 11 (pp. 712-719). Springer International Publishing.
[15] Almubark, I., Chang, L.C., Shattuck, K.F., Nguyen, T., Turner, R.S. and Jiang, X., 2020. A 5-min cognitive task with deep learning accurately detects early Alzheimer's disease. Frontiers in aging neuroscience, 12, p.603179.
[16] Chatterjee, S., Saad, F., Sarasaen, C., Ghosh, S., Krug, V., Khatun, R., Mishra, R., Desai, N., Radeva, P., Rose, G. and Stober, S., 2024. Exploration of interpretability techniques for deep covid-19 classification using chest x-ray images. Journal of Imaging, 10(2).
[17] Althaqafi, T., Al-Ghamdi, A.S.A.M. and Ragab, M., 2023, April. Artificial intelligence based COVID-19 detection and classification model on chest X-ray images. In Healthcare (Vol. 11, No. 9, p. 1204). MDPI.
[18] Kumar, M., SL, S.D. and Prashanth, B.S., 2024. CXNet-A Novel approach for COVID-19 detection and Classification using Chest X-Ray image. Procedia Computer Science, 235, pp.2486-2497.
[19] Islam, M.K., Rahman, M.M., Ali, M.S., Miah, M.S. and Rahman, M.H., 2023. An Enhanced Technique of COVID‐19 Detection and Classification Using Deep Convolutional Neural Network from Chest X‐Ray and CT Images. BioMed Research International, 2023(1), p.6341259.
[20] Hilali-Jaghdam, I., Elhag, A.A., Ishak, A.B., Elnaim, B.M.E., Elhag, O.E.M., Abuhaimed, F.M. and Abdel-Khalek, S., 2024. Towards COVID-19 detection and classification using optimal efficient Densenet model on chest X-ray images. Alexandria Engineering Journal, 101, pp.136-146.
[21] Balasubramaniam, S. and Kumar, K.S., 2023. Optimal Ensemble learning model for COVID-19 detection using chest X-ray images. Biomedical Signal Processing and Control, 81, p.104392.
[22] https://github.com/ieee8023/covid-chestxray-dataset
[23] Sharmin, S., Ahammad, T., Talukder, M.A. and Ghose, P., 2023. A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection. IEEE Access
[24] Wang, Z., Yang, M., Jiao, Q., Xu, L., Han, B., Li, Y. and Tan, X., 2024. Two-Level Spatio-Temporal Feature Fused Two-Stream Network for Micro-Expression Recognition. Sensors, 24(5), p.1574
[25] Saqlain, M., Riaz, M., Saleem, M.A. and Yang, M.S., 2021. Distance and similarity measures for neutrosophic hypersoft set (NHSS) with construction of NHSS-TOPSIS and applications. IEEE Access, 9, pp.30803-30816.
[26] Akbulut, Y., Sengur, A., Guo, Y. and Smarandache, F., 2017. NS-k-NN: Neutrosophic set-based k-nearest neighbors classifier. Symmetry, 9(9), p.179.
