Journal: Frontiers in Big Data

Article Title: Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis

doi: 10.3389/fdata.2024.1402926

Figure Lengend Snippet: Types of models used and their specifications.

Article Snippet: Sakthiraj ( ) , Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) algorithm , HCNN-IASO , No , Softmax-CNN layer classifier (based on ResNet-34 and DenseNet-121) , IV , -Before augmentation: Healthy: 190 CML: 58 CLL: 30 AML: 56 ALL: 182 -After augmentation: Healthy: 1,291 CML: 1,244 CLL: 845 AML: 1,198 ALL: 1,082 , The proposed approach is used to generate results and to accurately identify and detect them. The data augmentation technique involved is utilized to practice big datasets and thus it processes large Leukemia images. The features from Leukemia datasets are extracted by using our proposed HCNN and further the attention layer in the HCNN is exploited to fuse the extracted features. The softmax layer of HCNN acts as a classifier and therefore it classifies the leukemia dataset into several subtypes. Furthermore, the accuracy of classification is optimized by utilizing Interactive autodidactic school optimization techniques. Finally, the optimized outcomes are sent to the medical institution/hospital via an IoMT platform for further processing. Based on the results retrieved, the physician/doctor provides a diagnosis to the patients..

Techniques: Biomarker Discovery, Derivative Assay, Microscopy, Staining, Diagnostic Assay, Control, Generated

Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Mean Squared Error analysis for different FE techniques without feature selection.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques: Selection

Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Mean Squared Error analysis for different feature extraction techniques with Bald Eagle Search Optimization (BESO) feature selection.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques: Extraction, Selection

Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Mean Squared Error analysis for different feature extraction techniques with Red Deer Optimization (RDO) feature selection.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques: Extraction, Selection

Journal: Bioengineering

Article Title: Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

doi: 10.3390/bioengineering11080766

Figure Lengend Snippet: Classifiers optimal parameters.

Article Snippet: An interesting observation is the significant reduction in Test MSE for SDC (Softmax Discriminant Classifier) compared to the previous table (Train MSE: 2.21 × 10 −5 , Test MSE: 1.6 × 10 −5 to Train MSE: 2.81 × 10 −6 , Test MSE: 2.81 × 10 −4 ).

Techniques: