Lung cancer detection using hybrid integration of autoencoder feature extraction and ML techniques

Lung cancer posed a significant global health challenge, necessitating innovative approaches for early detection and accurate diagnosis. In this paper, CT scan images for lung cancer with three classes namely benign, malignant, and normal are collected from Kaggle. We initially applied conventional machine learning (ML) algorithms including support vector machine (SVM), random forests (RF), decision trees (DT), logistic regression (LR), naive bayes (NB), and k-nearest neighbor for lung cancer detection. The results with these conventional algorithms are recorded. Later, we proposed a novel hybrid model that integrated diverse machine learning algorithms to further enhance accuracy. Our approach combined the power of autoencoders for feature extraction. Using Autoencoder technique, features from images are extracted and a new feature vector is created. Later, the same conventional ML classifiers applied and achieved enhanced performance. The hybrid model demonstrated remarkable performance in identifying lung cancer cases when compared to individual classifiers. Through extensive experimentation, we showcased the efficacy of our integrated framework, achieving high accuracy, precision, recall and F1-score metrics across multiple classifiers. This hybrid approach represented a significant advancement in lung cancer detection, offering a versatile and robust solution for early diagnosis and personalized treatment strategies in clinical settings.