In Depth Analysis of Lung Disease Prediction Using Machine Learning Algorithms

Sen, Ishan; Hossain, Md. Ikbal; Shakib, Md. Faisal Hossan; Imran, Md. Asaduzzaman; Faisal, Faiz Al

doi:10.1007/978-981-15-6318-8_18

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…However, this method needs to more robust automatic segmentation. Sen et al [7] investigated to foresee the lung diseases with K-fold cross-validation and specially used five machine learning algorithms including Bagging, LR, RF, Logistic Model Tree, and Bayesian Networks. The accuracy of these algorithms was 88%, 88.92%, 90.15%, 89.23%, and 83.69%, respectively.…”

Section: Traditional Approachesmentioning

confidence: 99%

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An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

2021

Turk J Elec Eng & Comp Sci

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The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9% that outperforms three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy.

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Section: Traditional Approachesmentioning

confidence: 99%

“…Problem Statement and Motivation. Some researchers already worked for detecting and classifying lung diseases [5][6][7][8][9][10][11]. Machine learning-based algorithms such as k-nearest neighbors (KNN) [12], Bayesian [13], random forest (RF) [6], support vector machine (SVM) [14], and so on were used in their works.…”

Section: Introductionmentioning

confidence: 99%