Wireless Capsule Endoscopy Bleeding Images Classification Using CNN Based Model

Rustam, Furqan; Siddique, Muhammad Abubakar; Siddiqui, Hafeez Ur Rehman; Ullah, Saleem; Mehmood, Arif; Ashraf, Imran; Choi, Gyu Sang

doi:10.1109/access.2021.3061592

Cited by 64 publications

(26 citation statements)

References 33 publications

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“…Owing to the importance of the healthcare domain, several research works can be found in the literature that focus on cancer prediction using machine and deep learning approaches. For example, studies 10 , 11 perform cancer prediction using image-based approaches. Similarly, Goutam et al 4 developed an automated system for the diagnosis of leukemia.…”

Section: Related Workmentioning

confidence: 99%

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Rupapara

Rustam

Aljedaani

et al. 2022

Sci Rep

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Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for its accurate prediction is of significant importance. Diagnosis of blood cancer using leukemia microarray gene data and machine learning approach has become an important medical research today. Despite research efforts, desired accuracy and efficiency necessitate further enhancements. This study proposes an approach for blood cancer disease prediction using the supervised machine learning approach. For the current study, the leukemia microarray gene dataset containing 22,283 genes, is used. ADASYN resampling and Chi-squared (Chi2) features selection techniques are used to resolve imbalanced and high-dimensional dataset problems. ADASYN generates artificial data to make the dataset balanced for each target class, and Chi2 selects the best features out of 22,283 to train learning models. For classification, a hybrid logistics vector trees classifier (LVTrees) is proposed which utilizes logistic regression, support vector classifier, and extra tree classifier. Besides extensive experiments on the datasets, performance comparison with the state-of-the-art methods has been made for determining the significance of the proposed approach. LVTrees outperform all other models with ADASYN and Chi2 techniques with a significant 100% accuracy. Further, a statistical significance T-test is also performed to show the efficacy of the proposed approach. Results using k-fold cross-validation prove the supremacy of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Rupapara

Rustam

Aljedaani

et al. 2022

Sci Rep

Self Cite

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“…The dense layer consists of neurons, and the inputs of these neurons are associated weights; after performing some linear functions, they pass outputs to the next layer [65]. All the neurons of a dense layer are connected to the input and output layers.…”

Section: Dense Layermentioning

confidence: 99%

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach

Hashmi

Ibrahim

Bajwa

et al. 2022

Sensors

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The periodic inspection of railroad tracks is very important to find structural and geometrical problems that lead to railway accidents. Currently, in Pakistan, rail tracks are inspected by an acoustic-based manual system that requires a railway engineer as a domain expert to differentiate between different rail tracks’ faults, which is cumbersome, laborious, and error-prone. This study proposes the use of traditional acoustic-based systems with deep learning models to increase performance and reduce train accidents. Two convolutional neural networks (CNN) models, convolutional 1D and convolutional 2D, and one recurrent neural network (RNN) model, a long short-term memory (LSTM) model, are used in this regard. Initially, three types of faults are considered, including superelevation, wheel burnt, and normal tracks. Contrary to traditional acoustic-based systems where the spectrogram dataset is generated before the model training, the proposed approach uses on-the-fly feature extraction by generating spectrograms as a deep learning model’s layer. Different lengths of audio samples are used to analyze their performance with each model. Each audio sample of 17 s is split into 3 variations of 1.7, 3.4, and 8.5 s, and all 3 deep learning models are trained and tested against each split time. Various combinations of audio data augmentation are analyzed extensively to investigate models’ performance. The results suggest that the LSTM with 8.5 split time gives the best results with the accuracy of 99.7%, the precision of 99.5%, recall of 99.5%, and F1 score of 99.5%.

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“…In Reference 21, CNNs were assessed based on accuracy where mobileNet‐V2, Inception‐V3 and Nasnet‐Mobile were distinguished among the ones achieving higher accuracy, which outperformed several well‐known ones such as SqueezeNet, ShuffleNet, AlexNet and GoogleNet. Those CNN models succeeded in achieving efficient results in recent interesting studies on different biomedical imaging domains, 22 such as the detection of systemic sclerosis 23 and gastrointestinal abnormalities 24 using MobileNet‐V2, the diagnosis of prostate cancer 25 and the examination of pulmonary nodules 26 using Inception‐V3, or the prediction of the Gleason score 27 and the identification of gastrointestinal diseased tissues 28 using Nasnet‐Mobile. Apart from the accurate results, the clinical context involves timing constraints in order to ensure the use of computer vision tools.…”

Section: Ensemble Learning Framework For Cataract Severity Gradingmentioning

confidence: 99%

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

Elloumi

2022

Int J Imaging Syst Tech

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The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately.

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Wireless Capsule Endoscopy Bleeding Images Classification Using CNN Based Model

Cited by 64 publications

References 33 publications

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

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