New human identification method using Tietze graph-based feature generation

Tuncer, Türker; Aydemir, Emrah; Doğan, Şengül; Kobat, Mehmet Ali; Kaya, M. Çağrı; Metin, Serkan

doi:10.1007/s00500-021-06094-5

Cited by 3 publications

(1 citation statement)

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“…From each final merged feature vector of length 86 016 obtained per input image, we used INCA 45 to automatically select a vector with the optimal number of the most discriminative features. [49][50][51] The INCA function has two parameters: loop and misclassification rate calculator.…”

Section: Feature Selectionmentioning

confidence: 99%

Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images

Erdem,

Kobat,

Bilen

et al. 2023

Int J Imaging Syst Tech

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COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.

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Section: Feature Selectionmentioning

confidence: 99%

Hybrid‐Patch‐Alex: A new patch division and deep feature extraction‐based image classification model to detect COVID‐19, heart failure, and other lung conditions using medical images

Erdem,

Kobat,

Bilen

et al. 2023

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

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Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

Dogan,

Barua,

Baygin

et al. 2024

Cogn Neurodyn

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This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

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