Improved transfer learning using textural features conflation and dynamically fine-tuned layers.

ABSTRACT: Transfer learning involves using previously learnt knowledge of a model task in addressing another task. However, this process works well when the tasks are closely related. It is, therefore, important to select data points that are closely relevant to the previous task and fine-tune the suitable pre-trained model's layers for effective transfer. This work utilises the least divergent textural features of the target datasets and pre-trained model's layers, minimising the lost knowledge during the transfer learning process. This study extends previous works on selecting data points with good textural features and dynamically selected layers using divergence measures by combining them into one model pipeline. Five pre-trained models are used: ResNet50, DenseNet169, InceptionV3, VGG16 and Mob

SUBMITTER: Wanjiku RN 

PROVIDER: S-EPMC10557498 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature