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Image Search Results


Flow of the Xception architecture, illustrating the depthwise separable convolutions that optimize computational efficiency while maintaining high accuracy .

Journal: Life

Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

doi: 10.3390/life15030327

Figure Lengend Snippet: Flow of the Xception architecture, illustrating the depthwise separable convolutions that optimize computational efficiency while maintaining high accuracy .

Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

Techniques:

Performance assessment of transfer learning: Xception.

Journal: Life

Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

doi: 10.3390/life15030327

Figure Lengend Snippet: Performance assessment of transfer learning: Xception.

Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

Techniques:

Performance matrices for base model + transfer learning.

Journal: Life

Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

doi: 10.3390/life15030327

Figure Lengend Snippet: Performance matrices for base model + transfer learning.

Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

Techniques: Biomarker Discovery

Performance assessment of fine-tuned transfer learning: Xception.

Journal: Life

Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

doi: 10.3390/life15030327

Figure Lengend Snippet: Performance assessment of fine-tuned transfer learning: Xception.

Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

Techniques:

Performance matrices for fine-tuned transfer learning model.

Journal: Life

Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

doi: 10.3390/life15030327

Figure Lengend Snippet: Performance matrices for fine-tuned transfer learning model.

Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

Techniques: Biomarker Discovery

Comparison with other state-of-art model.

Journal: Life

Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

doi: 10.3390/life15030327

Figure Lengend Snippet: Comparison with other state-of-art model.

Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

Techniques: Comparison

Prediction for tumor (1/0) using fine-tuned transfer learning: Xception.

Journal: Life

Article Title: Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks

doi: 10.3390/life15030327

Figure Lengend Snippet: Prediction for tumor (1/0) using fine-tuned transfer learning: Xception.

Article Snippet: Proposed , Fine-Tuned Transfer Learning Xception , Kaggle , 0.9611.

Techniques:

A summary of DR prescreening techniques and reported performances.

Journal: Scientific Reports

Article Title: Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and Chi 2 feature ranking

doi: 10.1038/s41598-025-90048-6

Figure Lengend Snippet: A summary of DR prescreening techniques and reported performances.

Article Snippet: Bhuiyan et al. , The cloud-based screening model was developed. Neural network architectures and logistic model trees: Xception, Inception-V3, Inception-Resnet-V2 and Logistic model trees (LMT) were employed , 88,702 images from Kaggle dataset , Sensitivity , 99.21.

Techniques: Biomarker Discovery, Plasmid Preparation, Diagnostic Assay, Software, Extraction, Selection