A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease

Aqeel, Anza; Hassan, Ali; Khan, Muhammad Attique; Rehman, Saad; Tariq, Usman; Kadry, Seifedine; Majumdar, Arnab; Thinnukool, Orawit

doi:10.3390/s22041475

Cited by 32 publications

(16 citation statements)

References 48 publications

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“…Therefore, the improved optimization algorithm is developed. In the future, the following points shall be considered: i) Advanced CNN model shall be opted for deep features extraction [29,30]; ii) Model shall be train on noisy and clean images to check the capability of designed CNN architecture [31][32][33], and iii) More datasets will be utilized for the experimental process and consider reinforcement learning technique.…”

Section: Discussionmentioning

confidence: 99%

Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

Abbas¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

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Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets-Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time.

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Section: Discussionmentioning

confidence: 99%

Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

Abbas¹,

Khan²,

Alhaisoni³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

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“…In computer vision and machine learning deep CNN model are vigorously used for classification [33,34]. In deep CNN layers architecture, the image features can be targeted and process in following layer as shown in Fig.…”

Section: Classificationmentioning

confidence: 99%

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

Ali¹,

Shah²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

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Tumor detection has been an active research topic in recent years due to the high mortality rate. Computer vision (CV) and image processing techniques have recently become popular for detecting tumors in MRI images. The automated detection process is simpler and takes less time than manual processing. In addition, the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians. We proposed a new framework for tumor detection as well as tumor classification into relevant categories in this paper. For tumor segmentation, the proposed framework employs the Particle Swarm Optimization (PSO) algorithm, and for classification, the convolutional neural network (CNN) algorithm. Popular preprocessing techniques such as noise removal, image sharpening, and skull stripping are used at the start of the segmentation process. Then, PSO-based segmentation is applied. In the classification step, two pre-trained CNN models, alexnet and inception-V3, are used and trained using transfer learning. Using a serial approach, features are extracted from both trained models and fused features for final classification. For classification, a variety of machine learning classifiers are used. Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent, respectively, whereas average jaccard values are 96.30 percent and 96.57% (Segmentation Results). The results were extended on the same datasets for classification and achieved 99.0% accuracy, sensitivity of 0.99, specificity of 0.99, and precision of 0.99. Finally, the proposed method is compared to state-of-the-art existing methods and outperforms them.

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“…In addition, 1.80 million people died from lung cancer in 2020 3 . Herein, there are studies that can be useful in the literature from Alzheimer's disease to skin cancer 4–6 . When lung cancer statistics are examined, it is seen that most of them include small cell lung cancer and non‐small cell lung cancer types.…”

Section: Introductionmentioning

confidence: 99%

“…3 Herein, there are studies that can be useful in the literature from Alzheimer's disease to skin cancer. [4][5][6] When lung cancer statistics are examined, it is seen that most of them include small cell lung cancer and non-small cell lung cancer types. Herein, approximately 13% of all lung cancer types are small cell lung cancer, while 84% are non-small cell lung cancer.…”

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confidence: 99%

Lung cancer detection by using probabilistic majority voting and optimization techniques

Sünnetci

Alkan

2022

Int J Imaging Syst Tech

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The number of people dying from lung cancer in the world is increasing day by day. Therefore, early diagnosis of lung cancer holds a prominent position for the recovery of patients. Chest computed tomography scan images that are clinical adjuncts are used in the diagnosis of lung cancer. Herein, the proposed method automatically diagnoses lung cancer using machine learning algorithms, probabilistic majority voting, and optimization techniques. The paper uses images from Adenocarcinoma Left Lower Lobe, Large Cell Carcinoma Left Hilum, Normal, and Squamous Cell Carcinoma Left Hilum subjects. 80% and 20% of these images are used in the training and validation set, respectively, and there are 1000 images in the data set. For each image used in the study, image features are extracted using the Bag of Features method (2000 features). Linear Discriminant, Optimizable Support Vector Machine, and Optimizable K-Nearest Neighbor (KNN) classifiers are trained using these features. Afterward, the new image label is estimated in two steps. First, if the two or three classifier outputs are the same, the image label is detected using the majority voting technique. Second, if the classifier outputs are not the same, the image label is determined according to the most successful classifier, Optimizable KNN. To better experience the majority voting technique used in the proposed method, its detailed theoretical framework is given in the study.Additionally, the Graphical User Interface application of the proposed method has been designed to save time and reduce the workload of expert radiologists.From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F 1 Score (%), Matthews Correlation Coefficient, and Kappa of the proposed method are equal to 99.2837, 0.007163, 99.2571, 99.9139, 99.3486, 99.2816, 0.98738, and 0.94965 respectively.

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A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease

Cited by 32 publications

References 48 publications

Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

Crops Leaf Diseases Recognition: A Framework of Optimum Deep Learning Features

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

Lung cancer detection by using probabilistic majority voting and optimization techniques

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