An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images

Zadeh, Firoozeh Abolhasani; Ardalani, Mohammadreza Vazifeh; Salehi, Ali Rezaei; Farahani, Roza Jalali; Hashemi, Mandana; Mohammed, Adil Hussein

doi:10.1155/2022/3035426

Cited by 3 publications

(3 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Integration of spectral bands with spectral, textural, morphological, and contextual features could improve classification accuracy because each land use/cover has a distinct shape, size, tone, and texture on the satellite image [51,52]. The vast number of variables involved in one sophisticated data analysis could also improve the classification accuracy [53]. In this study, a total 14 features (e.g., 02 spectral, 12 texture) were extracted and used for LULC classification, which has been explained below.…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Barman,

Mustak,

Kuffer

et al. 2023

Sustainability

View full text Add to dashboard Cite

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

“…The Gray level co-occurrence matrix (GLCM) are second order's statistical texture characteristics from an image [53]. The GLCM are robust texture features widely used in improving the LULC classification [57].…”

mentioning

confidence: 99%

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Barman,

Mustak,

Kuffer

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The image scanning methods are categorized into Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). These image scanning methods are differentiated by its volume level of radiation to screen the various regions of brain [8][9][10][11]. The radiation is important for scanning the internal organs of the human body and also it affects the soft organ cell patterns due to its high level of intensity.…”

Section: Introductionmentioning

confidence: 99%

Detection and Segmentation of Meningioma Tumors Using the Proposed MENCNN Model

John Nisha Anita,

Sujatha Kumaran

2023

ARASET

View full text Add to dashboard Cite

This paper develops a Meningioma Detection and Segmentation System (MDSS) using the proposed Meningioma Convolutional Neural Network (MENCNN) classifier. The main objective of this paper is to detect and locate the meningioma brain tumors using the proposed deep learning structure and segmentation algorithm. This proposed MDSS is designed with preprocessing of meningioma and healthy brain MRI images, feature computations and feature classification through the proposed MENCNN classifier and Meningioma Segmentation Algorithm. The noises in both meningioma and healthy brain images are removed using Mean Adaptive Filter (MAF) and the meningioma features are computed from the noise removed image. These meningioma features are classified by the proposed MENCNN classifier in order to obtain the classification results as either meningioma or healthy brain image. Finally, Meningioma Segmentation Algorithm (MSA) is proposed in this research work to segment the pixels belonging to the meningioma region. The proposed MDSS approach obtains 96.46% MSI, 97.75% MSR and 97.6% MSA on the set of meningioma images in Nanfang dataset. The proposed MDSS approach obtains 97.76% MSI, 98.03% MSR and 97.81% MSA on the set of meningioma images in Kaggle dataset.

show abstract

An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs

et al. 2023

View full text Add to dashboard Cite

A brain tumor is an uncontrolled malignant cell growth in the brain, which is denoted as one of the deadliest types of cancer in people of all ages. Early detection of brain tumors is needed to get proper and accurate treatment. Recently, deep learning technology has attained much attraction to the physicians for the diagnosis and treatment of brain tumors. This research presents a novel and effective brain tumor classification approach from MRIs utilizing AlexNet CNN for separating the dataset into training and test data along with extracting the features. The extracted features are then fed to BayesNet, sequential minimal optimization (SMO), Naïve Bayes (NB), and random forest (RF) classifiers for classifying brain tumors as no-tumor, glioma, meningioma, and pituitary tumors. To evaluate our model’s performance, we have utilized a publicly available Kaggle dataset. This paper demonstrates ROC, PRC, and cost curves for realizing classification performance of the models; also, performance evaluating parameters, such as accuracy, sensitivity, specificity, false positive rate, false negative rate, precision, f-measure, kappa statistics, MCC, ROC area, and PRC area, have been calculated for four testing options: the test data itself, cross-validation fold (CVF) 4, CVF 10, and percentage split (PS) 34% of the test data. We have achieved 88.75%, 98.15%, 86.25% and 100% of accuracy using the AlexNet CNN+BayesNet, AlexNet CNN+SMO, AlexNet CNN+NB, and AlexNet CNN+RF models, respectively, for the test data itself. The results imply that our approach is outstanding and very effective.

show abstract

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images

Cited by 3 publications

References 76 publications

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

Detection and Segmentation of Meningioma Tumors Using the Proposed MENCNN Model

An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs

Contact Info

Product

Resources

About