Abstract
With the recent advancements in medicine and biotechnology, there is always a growing need for more sophisticated decision systems to aid in certain biological factors, such as detecting a pathogen or microscopic organism and its accurate classification. In such a domain, there is very little tolerance for error, and the highest priority must be given to creating intelligent systems capable of doing so. Advancements in computer vision and other state-of-the-art image processing techniques enable computation systems to extract and analyze principal features from a specimen that even a trained eye may otherwise miss. There has also been an upsurge in the research on the applications of machine learning to aid in specific surgical procedures and diagnostic tests. Classifying organisms at the micro-scale is an essential stepping stone for their long-term success. The study aims to ensembling the best machine learning techniques and models in the computer vision domain, such as transfer learning, top-performing models, and image preprocessing methods, to maximize the capabilities of a typical image classification model for a sparse and limited multi-labeled class dataset containing microscopic images and hence shed light and explore into how more excellent performance can be obtained while facing classical dataset limitations.
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References
Li Z, Li C, Yao Y, Zhang J, Rahaman MM, Xu H et al (2021) Environmental microorganism image dataset fifth version for multiple image analysis tasks. PLoS ONE 16(5):99–110. https://doi.org/10.10007/1234567890
Waquar (2022) Micro-organism image classification. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4032122
Poomrittigul S, Chomkwah W, Tanpatanan T, Sakorntanant S, Treebupachatsakul T (2022) A comparison of deep learning CNN architecture models for classifying bacteria. In: 2022 37th International technical conference on circuits/systems, computers and communications (ITC-CSCC), pp 290–293. https://doi.org/10.1109/ITC-CSCC55581.2022.9894986
Chen W, Liu P, Lai C, Lin Y (2022) Identification of environmental microorganism using optimally fine-tuned convolutional neural network. Environ Res 206:112610. ISSN 0013-9351, https://doi.org/10.1016/j.envres.2021.112610
Kim HE, Maros ME, Siegel F, Ganslandt T (2022) Rapid convolutional neural networks for gram-stained image classification at inference time on mobile devices: empirical study from transfer learning to optimization. Biomedicines 10:2808. https://doi.org/10.3390/biomedicines10112808
Uma Venkata Ravi Teja K, Pavan Venkat Reddy B, Likith Preetham A, Patil HY (2021) Poorna Chandra T (2021) Prediction of diabetes at early stage with supplementary polynomial features. In: Smart technologies, communication and robotics (STCR), pp 1–5. https://doi.org/10.1109/STCR51658.2021.9588849
Uma Venkata Ravi Teja K, Pavan Venkat Reddy B, Alla LP, Patil HY (2021) Parkinson’s disease classification using quantile transformation and RFE. In: 2021 12th International conference on computing communication and networking technologies (ICCCNT), pp 01–05. https://doi.org/10.1109/ICCCNT51525.2021.9580024
Hebbar N, Patil HY, Agarwal K (2020) Web powered CT scan diagnosis for brain hemorrhage using deep learning. In: 2020 IEEE 4th Conference on information & communication technology (CICT), pp 1–5. https://doi.org/10.1109/CICT51604.2020.9312098
Patil P, Patil H (2020) X-ray imagining based pneumonia classification using deep learning and adaptive clip limit based CLAHE algorithm. In: 2020 IEEE 4th Conference on information & communication technology (CICT), pp 1–4. https://doi.org/10.1109/CICT51604.2020.9312089
Wang F, Ma M, Cao H, Chai X, Huang M, Liu L (2022) Conjugated polymer materials for detection and discrimination of pathogenic microorganisms: guarantee of biosafety. Biosaf Health 4(2):79–86. ISSN 2590-0536, https://doi.org/10.1016/j.bsheal.2022.03.006
Ma P, Li C, Rahaman MM et al (2022) A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. Artif Intell Rev. https://doi.org/10.1007/s10462-022-10209-1
Kulwa F, Li C, Grzegorzek M, Rahaman M, Shirahama K, Kosov S (2023) Segmentation of weakly visible environmental microorganism images using pair-wise deep learning features. Biomed Signal Process Control 79(Part 2):104168. ISSN 1746–8094. https://doi.org/10.1016/j.bspc.2022.104168
Zhang J, Ma P, Jiang T, Zhao X, Tan W, Zhang J, Zou S, Huang X, Grzegorzek M, Li C (2022) SEM-RCNN: a squeeze-and-excitation-based mask region convolutional neural network for multi-class environmental microorganism detection. Appl Sci 12:9902. https://doi.org/10.3390/app12199902
Prada P, Brunel B, Reffuveille F, Gangloff SC (2022) Technique evolutions for microorganism detection in complex samples: a review. Appl Sci 12:5892. https://doi.org/10.3390/app12125892
Shao R, Bi X-J, Chen Z (2022) A novel hybrid transformer-CNN architecture for environmental microorganism classification. PLoS ONE 17(11):e0277557. https://doi.org/10.1371/journal.pone.0277557
Zhang J, Li C, Yin Y et al (2022) Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artif Intell Rev. https://doi.org/10.1007/s10462-022-10192-7
Rani P, Kotwal S, Manhas J et al (2022) Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and developments. Arch Comput Methods Eng 29:1801–1837. https://doi.org/10.1007/s11831-021-09639-x
Kulwa F et al (2019) A state-of-the-art survey for microorganism image segmentation methods and future potential. IEEE Access 7:100243–100269. https://doi.org/10.1109/ACCESS.2019.2930111
Narain Ponraj D et al (2011) A survey on the preprocessing techniques of mammogram for the detection of breast cancer. J Emerg Trends Comput Inf Sci 2(12):656–664
Chao D, Loy Change C, Kaiming H, Xiaoou T (2014) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38. https://doi.org/10.1109/TPAMI.2015.2439281
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Chettiar, G., Shukla, A., Patil, H., Jindal, S. (2023). Classification of Microorganisms from Sparsely Limited Data Using a Proposed Deep Learning Ensemble. In: Suma, V., Lorenz, P., Baig, Z. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-99-1624-5_22
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