Abstract
Lung cancer remains one of the deadliest diseases in the world and early detection is critical to enhancing survival rates. With traditional diagnostic techniques - CT scans and chest X-rays - an invasive procedure must be performed and, in some cases, it relies on expert interpretation. Whether benign or malignant, the similarities in visual characteristics of nodules leads to ambiguity and makes for a difficult case which calls for the development of automatic lung cancer classification framework such as the one we proposed, which incorporates Deep Learning (DL) methods and uses a rigourous training methodology on top of that. Our framework pre-processes the images with adaptive filters to eliminate noise, segments lesions, removes, and refines features with Hybrid Horse Herd Optimization (HHO) and Lion Optimization Algorithm (LOA). Those features are classified with a hybrid Deep Convolutional Neural Network and Long Short-Term Memory (DCNN + LSTM) model, which jointly enhances features extraction and temporal learning. We run data learning against standard lung CT datasets and achieved a score of 98.75% accuracy, demonstrating the proposed system is effective in classifying normal lung tissue from abnormal. Nonetheless, the real-time usability of the system is limited by the performance of the CT, and the computational demands of the model, which can be troublesome for clinical situations that typically possess less computational power. Furthermore, these limitations never the less provide a more intelligent, accurate diagnostic aid for radiologists that non-invasively assists in clinical decision making and, importantly, earlier cancer diagnoses.