Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Ahmed, Shaymaa Taha; Kadhem, Suhad Malallah

doi:10.3991/ijim.v15i16.24191

Cited by 25 publications

(23 citation statements)

References 37 publications

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“…The SVM is utilized to classify a feature after they have been selected, which allows for a method for extracting characteristics that is both effective and efficient as well as a set of criteria for classifier [25]. A unique hyper-plane depicts the discriminative classification technique SVM [26].…”

Section: Classifier Using Support Vector Machine (Svm)mentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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Alzheimer's Disease (AD), which is left untreated, is the primary cause of neurodegenerative dementia, which primarily affects those over 65. Memory and thought processes are gradually disrupted by the irreversible brain disruption and, eventually, the capacity to fulfil simple tasks. Detecting AD early prevents its progression and diminishes its signs. The study aimed to build a classification model that might predict the early stages of Alzheimer's disease using accurate early-stage gene expression data from blood obtained from a clinical Alzheimer's dataset. The datasets used in this work were gathered from the Gene Expression Omnibus (GEO) and are GSE63060 and GSE63061.It has the right rows for (569 samples) and columns for (16382 genes). The suggested GWO gene selection method aims to identify the ideal feature subset for medical data. A model for predicting early Alzheimer's disease is proposed based on the modified Grey Wolf Optimizer and Support Vector Machine (GWO-SVM). These methods will help reduce the number of trivial and redundant genes in the original datasets. We attained an accuracy of 82%-88% using only the Support Vector Machine method. When we utilized several evolutionary algorithms to implement gene selection, we observed an increase in accuracy of 6%-11%, with modified Grey Wolf optimization with Crossover (CGWO) called the proposed algorithm (CGWO-SVM) achieving the highest accuracy of (97.41%) with the Alzheimer's disease dataset in comparison to the other competitive schemes in the existing literature. Thus, the proposed system is suitable for picking more accurate classification and interesting genes to increase classification accuracy while decreasing gene dimensions.

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Section: Classifier Using Support Vector Machine (Svm)mentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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“…Following feature selection, the classifier is performed using SVM to extract features effectively and a set of rules to do classification. A distinct hyper-plane represents SVM, which is a discriminative classification algorithm [33], [34]. Figure 8 illustrates it.…”

Section: Classification Using Support Vector Machinementioning

confidence: 99%

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

Ahmed

Kadhem

2023

IJECE

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<span lang="EN-US">The problem with using microarray technology to detect diseases is that not each is analytically necessary. The presence of non-essential gene data adds a computing load to the detection method. Therefore, the purpose of this study is to reduce the high-dimensional data size by determining the most critical genes involved in Alzheimer's disease progression. A study also aims to predict patients with a subset of genes that cause Alzheimer's disease. This paper uses feature selection techniques like information gain (IG) and a novel metaheuristic optimization technique based on a swarm’s algorithm derived from nomadic people’s behavior (NPO). This suggested method matches the structure of these individuals' lives movements and the search for new food sources. The method is mostly based on a multi-swarm method; there are several clans, each seeking the best foraging opportunities. Prediction is carried out after selecting the informative genes of the support vector machine (SVM), frequently used in a variety of prediction tasks. The accuracy of the prediction was used to evaluate the suggested system's performance. Its results indicate that the NPO algorithm with the SVM model returns high accuracy based on the gene subset from IG and NPO methods.</span>

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“…The results obtained were good, reaching an accuracy of 88% when 5 stress classes were used, but 97% when these 5 classes were divided into 2 with the labels "No stress'' (classes 1 and 2) and "Stress" (classes 3, 4 and 5). The authors, in [11] developed a CNN model to segment the various types of breast abnormalities, based on the pretrained ResNet 50 model, achieving a recognition rate of 88%. Similarly, in [12] used 3 CNN models, Inception V3, Inception-ResNet V2 and ResNet-101, to predict whether patients with primary breast cancer will metastasize, based on their ultrasound images, the results were compared with the performance of 5 radiologists, having positive results in the two tests performed, in A and B with an area under the receiver operating characteristic curve (AUC) of 0.…”

Section: Related Workmentioning

confidence: 99%

Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

Guevara-Ponce,

Roque-Paredes,

Zerga-Morales

et al. 2023

IJACSA

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Breast cancer is the leading cause of mortality in women worldwide. One of the biggest challenges for physicians and technological support systems is early detection, because it is easier to treat and establish curative treatments. Currently, assistive technology systems use images to detect patterns of behavior with respect to patients who have been found to have some type of cancer. This work aims to identify and classify breast cancer using deep learning models and convolutional neural networks (CNN) with transfer learning. For the breast cancer detection process, 7803 real images with benign and malignant labels were used, which were provided by BreaKHis on the Kaggle platform. The convolutional basis (parameters) of pre-trained models VGG16, VGG19, Resnet-50 and Inception-V3 were used. The TensorFlow framework, keras and Python libraries were also used to retrain the parameters of the models proposed for this study. Metrics such as accuracy, error ratio, precision, recall and f1-score were used to evaluate the models. The results show that the models based on VGG16, VGG19 ResNet-50 and Inception-V3 obtain an accuracy of 88%, 86%, 97% and 96% respectively, recall of 84%, 82%, 96% and 96% respectively, in addition to f1-score of 86%, 83%, 96% and 95% respectively. It is concluded that the model that shows the best results is Resnet-50, obtaining high results in all the metrics considered, although it should be noted that the Inception-V3 model achieves very similar results in relation to Resnet-50, in all the metrics. In addition, these two models exceed the 95% threshold of correct results.

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Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Cited by 25 publications

References 37 publications

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

Detection of Breast Cancer using Convolutional Neural Networks with Learning Transfer Mechanisms

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