Optimal feature extraction and ulcer classification from WCE image data using deep learning

Masmoudi, Youssef; Ramzan, Muhammad; Khan, Sajid Ali; Habib, Mohammed

doi:10.1007/s00500-022-06900-8

Cited by 19 publications

(11 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly Ramzan et al [ 4 ] introduce a protection system for medical images; the author proposed a technique in which they secure medical images. Another medical base Optimal feature extraction and ulcer classification from WCE image data using deep-learning technique in introduced by [ 5 ] and another medical image-related work has been carried out by Azam et al [ 6 ] which is also related to health care domain. So, lot of studies have been continuing in the health care domain.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

Alazemi

Jehangir

Imran

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Lung cancer has the highest death rate of any other cancer in the world. Detecting lung cancer early can increase a patient’s survival rate. The corresponding work presents the method for improving the computer-aided detection (CAD) of nodules present in the lung area in computed tomography (CT) images. The main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classification, processing, and analysis of biomedical data. After the analysis, a model is proposed consisting of three main steps. In the first step, threshold values and component labeling of 3D components were used to segment the lung volume. In the second step, candidate nodules are identified and segmented with an optimal threshold value and rule-based trimming. It also selects 2D and 3D features from the candidate segmented node. In the final step, the selected features are used to train the SVM and classify the nodes and classify the non-nodes. To assess the performance of the proposed framework, experiments were performed on the LIDC data set. As a result, it was observed that the number of false positives in the nodule candidate was reduced to 4 FP per scan with a sensitivity of 95%.

show abstract

Section: Introductionmentioning

confidence: 99%

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

Alazemi

Jehangir

Imran

et al. 2023

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Improved results are achieved using 10-fold cross-validation. In future work, we plan to investigate the proposed model for Ulcer classification [31], medical records classification [32,33], and face analysis [34,35].…”

Section: Discussionmentioning

confidence: 99%

A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

Alhajlah¹

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to remove irrelevant features from these vectors. Finally, the optimized features are serially fused to pass them to the classifier. A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013, MMA, and CK+ to report performance based on various metrics. Accuracy results show that the proposed model has achieved 82.5% accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy. We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.

show abstract

“…Deep networks have attained high classification results easily since their architectures are effective [10][11][12]. In deep architecture, multilevel/multilayer most useful features are extracted and classified using the fine-tuned weights.…”

Section: Our Methodsmentioning

confidence: 99%

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Barua

Aydemir

Doğan

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

show abstract

Optimal feature extraction and ulcer classification from WCE image data using deep learning

Cited by 19 publications

References 37 publications

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Contact Info

Product

Resources

About