biostudies-literatureUnknown47(1)Bruntha PM<h4>Purpose</h4>In the field of medical diagnosis, deep learning-based computer-aided detection of diseases will reduce the burden of physicians in the diagnosis of diseases especially in the case of lung cancer nodule classification.<h4>Materials and methods</h4>A hybridized model which integrates deep features from Residual Neural Network using transfer learning and handcrafted features from the histogram of oriented gradients feature descriptor is proposed to classify the lung nodules as benign or malignant. The intrinsic convolutional neural network (CNN) features have been incorporated and they can resolve the drawbacks of handcrafted features that do not completely reflect the specific characteristics of a nodule. In the meantime, they also reduce the need for a large-scale annotatedJournal of medical physics1-9https://www.ebi.ac.uk/biostudies/studies/S-EPMC9084582biostudies-literatureA Novel Hybridized Feature Extraction Approach for Lung Nodule Classification Based on Transfer Learning Technique.PMC9084582Pandian SIAAnitha JAbraham SSKumar SNBruntha PMfalseA Novel Hybridized Feature Extraction Approach for Lung Nodule Classification Based on Transfer Learning Technique.<h4>Purpose</h4>In the field of medical diagnosis, deep learning-based computer-aided detection of diseases will reduce the burden of physicians in the diagnosis of diseases especially in the case of lung cancer nodule classification.<h4>Materials and methods</h4>A hybridized model which integrates deep features from Residual Neural Network using transfer learning and handcrafted features from the histogram of oriented gradients feature descriptor is proposed to classify the lung nodules as benign or malignant. The intrinsic convolutional neural network (CNN) features have been incorporated and they can resolve the drawbacks of handcrafted features that do not completely reflect the specific characteristics of a nodule. In the meantime, they also reduce the need for a large-scale annotated2022-01-01T00:00:00Z2022 Jan-Mar2025-04-04T10:01:34.781Z2025-04-04T10:01:34.781ZS-EPMC90845823554803710.4103/jmp.jmp_61_21