Deep Learning Model for Pathogen Classification Using Feature Fusion and Data Augmentation

Ahmad, Fadzil; Farooq, Amjad; Muhammad, Usman Ghani Khan

doi:10.2174/1574893615999200707143535

Cited by 12 publications

(5 citation statements)

References 116 publications

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“…Deep and machine learning procedures are generating outstanding results in the area of pathogen classification [27], bacterial identification [4], COVID19 [25]. They can help in the immediate and reliable diagnosis of diseases.…”

Section: Discussionmentioning

confidence: 99%

“…In our research, we utilied the DIBaS dataset [4] that comprises of annotated, high resolution, and microscopic images from thirty three species of bacteria. Out of these, 24 pathogenic species are selected, which are present in various environment's and cause different diseases [22]. Each strain consists of 20 images of a specific class.…”

Section: Dataset Details and Augmentationmentioning

confidence: 99%

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Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization

et al. 2023

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Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.

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Section: Discussionmentioning

confidence: 99%

Section: Dataset Details and Augmentationmentioning

confidence: 99%

Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization

et al. 2023

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“…The composition of k-spaced amino acid pairs (CKSAAP) ( Chen et al, 2010 ; Ahmad et al, 2021 ; Akbar et al, 2021 ; Al-Qazzaz et al, 2021 ; Alar and Fernandez, 2021 ; Alim et al, 2021 ; Buriro et al, 2021 ) method describes the order-related information of the protein sequence, which takes the occurrence frequency of two amino acids separated by k-residues in the sequence as a feature element. The protein contains 20 amino acids; thus, a 400-dimensional feature vector can be obtained for each interval.…”

Section: Methodsmentioning

confidence: 99%

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

Chen

2022

Front. Genet.

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The major histocompatibility complex (MHC) is a large locus on vertebrate DNA that contains a tightly linked set of polymorphic genes encoding cell surface proteins essential for the adaptive immune system. The groups of proteins encoded in the MHC play an important role in the adaptive immune system. Therefore, the accurate identification of the MHC is necessary to understand its role in the adaptive immune system. An effective predictor called PredMHC is established in this study to identify the MHC from protein sequences. Firstly, PredMHC encoded a protein sequence with mixed features including 188D, APAAC, KSCTriad, CKSAAGP, and PAAC. Secondly, three classifiers including SGD, SMO, and random forest were trained on the mixed features of the protein sequence. Finally, the prediction result was obtained by the voting of the three classifiers. The experimental results of the 10-fold cross-validation test in the training dataset showed that PredMHC can obtain 91.69% accuracy. Experimental results on comparison with other features, classifiers, and existing methods showed the effectiveness of PredMHC in predicting the MHC.

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“…Then, transfer learning was used in [33] with ResNet-18 [34] to detect longitudinal bacterial fission and in [35] with atrous convolution. Finally, [36] used various convolutional architectures to generate image representation which were then concatenated and classified with xgboost [37]. More detailed insights about microbe classification can be found in reviews [38]- [40].…”

Section: Related Workmentioning

confidence: 99%

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

Borowa

Rymarczyk

Ochońska

et al. 2023

IEEE J. Biomed. Health Inform.

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Preliminary microbiological diagnosis usually relies on microscopic examination and, due to the routine culture and bacteriological examination, lasts up to 11 days. Hence, many deep learning methods based on microscopic images were recently introduced to replace the time-consuming bacteriological examination. They shorten the diagnosis by 1-2 days but still require iterative culture to obtain monoculture samples. In this work, we present a feasibility study for further shortening the diagnosis time by analyzing polyculture images. It is possible with multi-MIL, a novel multi-label classification method based on multiple instance learning.To evaluate our approach, we introduce a dataset containing microscopic images for all combinations of four considered bacteria species. We obtain ROC AUC above 0.9, proving the feasibility of the method and opening the path for future experiments with a larger number of species.

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Deep Learning Model for Pathogen Classification Using Feature Fusion and Data Augmentation

Cited by 12 publications

References 116 publications

Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization

Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

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