Detection and classification of large bowel obstruction from <scp>X‐ray</scp> images using machine learning algorithms

Tamyalew, Yibeltal; Salau, Ayodeji Olalekan; Ayalew, Aleka Melese

doi:10.1002/ima.22800

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…The KNN classifier, on the other hand, was created for multiclass issues, therefore connecting it with the kernel function doesn't require additional work. In conclusion, as each method was examined on a different set of assumptions, the accuracy achieved on ensemble feature vectors is higher than the accuracy reached by each individual because the limitations of CNN with GLCM in texture feature is concealed ( 28 ).…”

Section: Discussionmentioning

confidence: 93%

Sputum smears quality inspection using an ensemble feature extraction approach

Kiflie

Tufa²,

Salau³

2023

Front. Public Health

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The diagnosis of tuberculosis (TB) is extremely important. Sputum smear microscopy is thought to be the best method available in terms of accessibility and ease of use in resource-constrained countries. In this paper, research was conducted to evaluate the effectiveness of tuberculosis diagnosis by examining, among other things, the underlying causes of sputum smear quality for Ethiopian states such as Tigray, Amahira, and Oromia. However, because it is done manually, it has its limitations. This study proposes a model for sputum smear quality inspection using an ensemble feature extraction approach. The dataset used was recorded and labeled by experts in a regional lab in Bahir Dar, near Felege Hiwot Hospital after being collected from Gabi Hospital, Felege Hiwot Hospital, Adit Clinic and Gondar Hospital, as well as Kidanemihret Clinic in Gondar. We used a controlled environment to reduce environmental influences and eliminate variation. All the data was collected using a smartphone (the standard 15) with a jpg file extension and a pixel resolution of 1,728 × 3,840. Prior to feature extraction, bicubic resizing, and ROI extraction using thresholding was performed. In addition, sequential Gaussian and Gabor filters were used for noise reduction, augmentation, and CLAHE was used for enhancement. For feature extraction, GLCM from the gray label and CNN from the color image were both chosen. Ultimately, when CNN, SVM, and KNN classifiers were used to test both CNN and GLCM features, KNN outperformed them all with scores of 87, 93, and 94% for GLCM, CNN, and a hybrid of CNN and GLCM, respectively. CNN with GLCM outperformed other methods by 0.7 and 0.1% for GLCM and CNN feature extractors using the same classifier, respectively. In addition, the KNN classifier with the combination of CNN and GLCM as feature extractors performed better than existing methods by 1.48%.

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Section: Discussionmentioning

confidence: 93%

Sputum smears quality inspection using an ensemble feature extraction approach

Kiflie

Tufa²,

Salau³

2023

Front. Public Health

Self Cite

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“…The predicted probability provides the basis for better model evaluation and selection. As such, using a machine learning model that predicts probability is generally preferred for imbalanced classification tasks [21][22][23][24][25][26].…”

Section: Effect Of Imbalanced Class Distributionmentioning

confidence: 99%

Influence of Class Imbalance and Resampling on Classification Accuracy of Chronic Kidney Disease Detection

Salau¹,

Markus²,

Assegie³

et al. 2023

MMEP

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Chronic kidney disease is one of the leading causes of death around the world. Early detection of chronic kidney disease is crucial to the reduction of mortality caused as a result of the disease. Machine learning methods are recently becoming popular for the detection of chronic kidney disease. This study investigates the influence of resampling for chronic kidney disease detection using an imbalanced chronic kidney disease dataset. Choosing an optimal feature subset for medical datasets is important for improving the performance of data-driven predictive models. The influence of imbalanced class distribution on predictive models has become an increasingly important topic due to the recent advances in automatic decision-making processes and the continuous expansion in the volume of the data collected by medical institutions. To address the identified research gap, an experimental evaluation of synthetic minority oversampling and near miss undersampling technique was performed on a real-world chronic kidney disease dataset using several classification methods such as decision tree, random forest, K-nearest neighbor, adaptive boosting, and support vector machine. The results demonstrate that a number of variables, including performance metrics, classification algorithm, and dataset characteristics, influence the best class distribution.The study also offers useful information about resampling methods for an imbalanced classification problem which will help improve classification accuracy.

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