Dataset selection process and exclusion criteria. حسب Seung Seog Han (4782486)

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm حسب Hussein Ali Bardan (21976208)

"…This strategy </p><p dir="ltr">not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, </p><p dir="ltr">leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection </p><p dir="ltr">process and CNNs for image classification, the proposed approach reduces computational costs while increasing </p><p dir="ltr">classification accuracy. …"

Dataset 2: Zip file containing the Tables of the presented method and results حسب Suchismita Behera (22027316)

Dataset 1: Zip file containing the figures of the presented methods and results in jpeg files حسب Suchismita Behera (22027316)

Min-Cut/Max-Flow Problem Instances for Benchmarking حسب Patrick Møller Jensen (7232042)

Visual representation of the combination of feature extraction approaches employed and classification algorithms to identify stomata. حسب Alexandre H. Aono (11603934)

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. حسب Gabriel Matías Lorenz (21094672)

PathOlOgics_RBCs Python Scripts.zip حسب Ahmed Elsafty (16943883)

"…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …"

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf حسب Cecilia Lindig-León (7889777)

Segmentation, trace denoising and spike extraction framework. حسب Changjia Cai (10647521)

"…The network predicts a probability of being a neuron, a bounding box and a binary mask for each candidate neuron taking summary images as inputs (mean and correlation). …"

Image_1_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.TIF حسب Dongying Zheng (8522289)

Image_2_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.tif حسب Dongying Zheng (8522289)

DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf حسب Francesca Piludu (10706391)

"…After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. …"

DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx حسب Francesca Piludu (10706391)

"…After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. …"

DataSheet_1_Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.docx حسب Shima Sepehri (11574997)

"…Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). …"