Resnet Transfer Learning For Enhanced Medical Image Classification In Healthcare

This work overcomes the limitations of sparsely labeled data by optimizing ResNet transfer learning methods in medical classification of images. Using a deductive approach along with interpretive philosophy, we optimize ResNet for better diagnostic performance on healthcare data sets. Our team of technical approach includes preprocessing datasets, configuring model architectures, and fine-tuning hyperparameters using secondary data. The improved model performance as demonstrated by the results is confirmed by metrics such as precision, reliability, and recall. Analyses of comparisons demonstrate superiority over basic models. Upcoming tasks include working together to create standardized benchmarks, improving interpretability along with scalability, and verifying in actual clinical settings.