Enhancing Lung Cancer Diagnosis through Advanced CT Scan Image Analysis: A Novel Approach using Mask-EffNet

Abstract

Identifying lung cancer can be done effectively by examining CT scan images. It is necessary to have an intelligent diagnostic system. The recent evolution of image categorization systems has been influenced by the use of deep convolution neural networks (CNN). In this research work, a new hybrid paradigm with images combines a modified deep transfer learning EfficientNet and a masked autoencoder for distribution estimation (MADE). By using MADE before classification in lung cancer classification can facilitate feature learning, dimensionality reduction, uncertainty estimation, handling imbalanced data, transfer learning, and model interpretability, ultimately leading to improved classification performance and better utilization of available data. The proposed model (Mask-EffNet) works in two phases. In the initial phase feature extraction is done by using MADE and the classification of different types is carried out in the subsequent phase using a pre-trained EfficientNet model. Mask-EffNet is tested using EfficientNetB7 variation. The research is conducted on the "IQ-OTH/NCCD" benchmark dataset, which consists of lung cancer patients classed as benign, malignant, or normal depending on whether they have lung cancer or not. The model Mask-EffNet attained an accuracy of 98.98% and a ROC score ranging from 0.9782 to 0.9872 on the test set. We examined the effectiveness for suggested pre-trained Mask-EffNet to that of various additional pre-trained CNN designs. The anticipated results show that the Mask-EffNet based on EfficientNetB7 prevails over different CNNs with regard of both accuracy as well as effectiveness.