Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

Al-Shourbaji, Ibrahim; Kachare, Pramod; Zogaan, Waleed; Muhammad, L. J.; Abualigah, Laith

doi:10.1007/s42979-022-01129-6

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…The number of hidden layers and the number of neurons in the hidden layers were investigated using the t-distributed random neighborhood embedding (t-SNE) technique. Finally, the effectiveness and superiority of the model were verified experimentally [4]. Quist et al presented a breast cancer diagnostic model that employed a permuted random forest (RF) technique.…”

Section: Introductionmentioning

confidence: 99%

An online belief rule base breast cancer diagnosis method with interpretability

Han

et al. 2023

Preprint

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Early diagnosis of breast cancer is critical for effective treatment. Artificial intelligence (AI) technology has shown promise in assisting physicians with diagnosis. However, the combination of qualitative and quantitative information in surveillance data leads to ambiguity and uncertainty. Belief rule bases (BRB) can address these issues by incorporating confidence distributions. However, existing BRB models rely on offline training and lack adaptability to changes in patient metrics. In addition, the ethical implications of using BRB for breast cancer diagnosis require attention to the interpretability of the model. Therefore, this paper presents an online belief rule base breast cancer diagnosis method with interpretability. The method uses online learning to achieve dynamic growth. It also overcomes the problem of interpretability loss in the optimization process by implementing interpretability constraints. The proposed method achieves competitive accuracy and interpretability in breast cancer diagnosis, as demonstrated by experiments using a large dataset of breast cancer cases.

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Section: Introductionmentioning

confidence: 99%

An online belief rule base breast cancer diagnosis method with interpretability

Han

et al. 2023

Preprint

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“…For multiclass classification, the number of class labels in the dataset was more than two. The dataset in this research belonged to the multiclass classification [20].…”

Section: Introductionmentioning

confidence: 99%

Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models

Alazaidah,

Samara,

Aljaidi

et al. 2023

Diagnostics

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Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research was to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aimed to meet three main objectives. These objectives were to identify the best regression model, the best classification model, and the best learning strategy that highly suited sleep disorder datasets. Considering two related datasets and several evaluation metrics that were related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty three regression models. Furthermore, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belonged to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

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“…Deep learning (DL) is one of the most powerful machine learning technologies that can automatically learn multiple features and patterns without human intervention [10][11][12]. DL enabled the building of predictive models for the early diagnosis of tumor disease, and scientists used proven pattern analysis methods.…”

Section: Introductionmentioning

confidence: 99%

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Alzu’bi

Abdullah

Hmeidi

et al. 2022

Journal of Healthcare Engineering

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Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist’s workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist’s workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease’s track and preserve the patient’s life.

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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

Cited by 16 publications

References 37 publications

An online belief rule base breast cancer diagnosis method with interpretability

An online belief rule base breast cancer diagnosis method with interpretability

Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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