Detecting High-risk Area for Lumpy Skin Disease in Cattle Using Deep Learning Feature

Yıl 2023, Cilt: 3 Sayı: 1, 27 - 35, 15.02.2023

Musa Genemo

https://doi.org/10.54569/aair.1164731

Cited By: 4

Öz

Cattle’s lumpy skin disease is a viral disease that transmits by blood-feeding insects like mosquitoes. The disease mostly affects animals that have not previously been exposed to the virus. Cattle lumpy skin disease impacts milk, beef, and national and international livestock trade. Traditional lumpy skin disease diagnosis is very difficult due to, the lack of materials, experts, and time-consuming. Due to this, it is crucial to use deep learning algorithms with the ability to classify the disease with high accuracy performance results. Therefore, Deep learning-based segmentation and classification are proposed for disease segmentation and classification by using deep features. For this, 10 layers of Convolutional Neural Networks have been chosen. The developed framework is initially trained on a collected Cattle’s lumpy Skin Disease (CLSD) dataset. The features are extracted from input images; hence the color of the skin is very important to identify the affected area during disease representation we used a color histogram. This segmented area of affected skin color is used for feature extraction by a deep pre-trained CNN. Then the generated result is converted into a binary using a threshold. The Extreme learning machine (ELM) classifier is used for classification. The classification performance of the proposed methodology achieved an accuracy of 0.9012% on CLSD To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

Anahtar Kelimeler

: Classification, Deep learning, Lumpy skin disease, Optimization, Segmentation

Kaynakça

• Thornton PK. Livestock production: recent trends, future prospects.” The Royal Society”, 56 (2010) 121-128.
• Thomas M. “Bayesian latent modeling of Spatio-temporal variation in small-area health data”. Theory Into Practice, 56 (2017) 121-128.
• Kang Y, Fang Y and Lai X, “Automatic detection of diabetic retinopathy with the statistical method and Bayesian classifier” J. Med. Imag. Health Information, 10(5) (2020) 1225–1233.
• Piekarski M, Jaworek-Korjakowska J, Wawrzyniak A.I, Gorgon M, “Convolutional neural network architecture for beam instabilities identification in Synchrotron Radiation Systems as an anomaly detection problem”. Measurement, 165 (2020) 108116.
• Nisa M, Shah J.H, Kanwal S, Raza M, Khan M.A, Damaševiˇcius R, Blažauskas T. “Hybrid malware classification method using segmentation-based fractal texture analysis and deep convolution neural network features” Appl. Sci. 10(14) (2020) 4966.
• Wei Z, Song H, Chen L, Li Q, Han G. “Attention-based DenseUnet network with adversarial training for skin lesion segmentation”. in IEEE Access, 7, (2019) 136616-136629; doi: 10.1109/ACCESS.2019.2940794.
• Tang J, Alelyani S, Liu H. “Feature selection for classification: A review. In Data Classification: Algorithms and Applications”, CRC Press: Boca Raton, FL, USA, (2014) 37–64.
• Genemo, M.D. “Suspicious activity recognition for monitoring cheating in exams”. Proc.Indian Natl. Sci. Acad. 88 (2022) 1–10.
• Farra D, Nardi MD, Lets V, Holopura S, Klymenok O, Stephan R, Boreiko O. “Qualitative assessment of the probability of introduction and onward transmission of lumpy skin disease in Ukraine”, Microbial Risk Analysis, 20 (2022), 100200; https://doi.org/10.1016/j.mran.2021.100200.
• Vigier, M., Vigier, B., Andritsch, E. et al. Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study. Sci Rep 11 (2021) 22292.
• Mehta P. and Shah B., “Review on techniques and steps of computer aided skin cancer diagnosis”, Procedia Comput. Sci., vol. 85, pp. 309–316, Jan. 2016.
• Abbas Q, García I.F, and Rashid M. “Automatic skin tumour border detection for digital dermoscopy using a new digital image analysis scheme”, Brit. J. Biomed. Sci., 67(4), (2010) 177–183.
• Lee T.K “Measuring border irregularity and shape of cutaneous melanocytic lesions”, Ph.D. dissertation, Simon Fraser Univ., Burnaby, BC, Canada, 2001.
• Schaefer G, Rajab M.I, Celebi M.E, and Iyatomi H, “Colour and contrast enhancement for improved skin lesion segmentation”, Computerized Med. Imag. Graph., 35(2), (2011), 99–104.
• Murugan A, Nair SAH, and Kumar KPS, “Detection of skin cancer using SVM, random forest and kNN classifiers”, J. Med. Syst., 43(8), (2019), 269.
• Yuan X, Yang Z, Zouridakis G, and Mullani N, “SVM-based texture classification and application to early melanoma detection”, in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., (2006), pp. 4775–4778.
• Kang C, Yu X, Wang S.-H, Guttery D. S, Pandey H. M, Tian Y., and Zhang Y.-D, “A heuristic neural network structure relying on fuzzy logic for images scoring”, IEEE Trans. Fuzzy Syst. Leicester, U.K.: Univ. of Leicester, School of Informatics, (2020), doi: 10.1109/TFUZZ.2020.2966163.
• Wang S, Sun J, Mehmood I, Pan C, Chen Y, and Zhang Y, “Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling”, Concurrency Comput., Pract. Exp., 32(1), (2020) p. e5130.
• Wang S, Tang C, Sun J, and Zhang Y, “Cerebral micro-bleeding detection based on densely connected neural network”, Frontiers Neurosci., 13, (2019), p. 422.
• Esteva A, Kuprel B, Novoa R.A, Ko J, Swetter S. M, Blau H. M, and Thrun S, “Dermatologist-level classification of skin cancer with deep neural networks”, Nature, 542(7639), (2017), 115–118.
• Al-masni M. A, Al-antari M. A, Choi M.-T, Han S.-M, and Kim T.-S, “Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks”, Comput. Methods Programs Biomed., 162, (2018), 221-231.
• Miglani V, Bhatia M. “Skin lesion classification: A transfer learning approach using efficientnets”, In Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), Jaipur, India, 13–15 February 2020, 315–324.
• Nachbar F, Stolz W, Merkle T, Cognetta A.B, Vogt T, Landthaler M, Bilck P, Braun-Falco O, and Plewig G, “ The ABCD rule of dermatoscopy: High Prospective value in the diagnosis of doubtful melanocytic skin lesion”, J.Amer.Acad.Dermatol., 30(4), (1994), 551-559.
• Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C. “Fusing fine-tuned deep features for skin lesion classification”, Comput. Med. Imaging Graph,71, (2019), 19–29.
• Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Martinez-Gonzalez P and Garcia-Rodriguez J, “A survey on deep learning techniques for image and video semantic segmentation”, Applied Soft Computing, 70, (2018), 41-65.
• Zujovic J, Gandy L, Friedman S, Pardo B and Pappas T.N, “Classifying paintings by artistic genre: An analysis of features & classifiers”, IEEE International Workshop on Multimedia Signal Processing, IEEE, (2009), 1-5.
• Kobylin O.A, Gorokhovatskyi V.О, Tvoroshenko I.S, and Peredrii O.О, “The application of non-parametric statistics methods in image classifiers based on structural description components”, Telecommunications and Radio Engineering, 79(10), (2020), 855-863.
• Dredze M, Gevaryahu R and Elias-Bachrach A. “Learning fast classifiers for image spam”, International Conference on Email and Anti-Spam, (2007).
• Gorokhovatskyi V.О, Tvoroshenko I.S and Vlasenko N.V, “Using fuzzy clustering in structural methods of image classification”, Telecommunications and Radio Engineering, 79(9), (2020), 781-791.