Modified ShuffleNet trained on gradient pattern and shape-based features for lung cancer classification with improved M-SegNet segmentation.

Lung cancer (LC) is one of the leading causes of death globally. Early detection is essential for saving lives and ensuring effective treatment for patients. When medical professionals can proactively diagnose and classify the condition, they can provide safer and more targeted interventions. The development of automated tools for early detection is vital to identify malignant states at their beginning. Despite the numerous algorithms used by researchers over the years, achieving high prediction accuracy remains a significant challenge. Considering this challenge, a novel Deep Learning (DL) model is proposed for lung cancer classification. The proposed method utilizes a novel Custom Mean Normalisation-based ShuffleNet (CMN-ShuffleNet) model designed for lung cancer classification. The method is divided into four key phases, including preprocessing, lobe segmentation, feature extraction, and classification. Initially, the input lung images undergo Histogram Equalization (HE) in the preprocessing step. Following this, the Modified Residual Recurrent based SegNet (mRRB-SegNet) model is employed for lobe segmentation. This modification improves the model’s capabilities and accuracy. From the segmented images, various features are extracted, which include Improved Local Gradient Pattern (ILGP), shape and statistical features. The final phase includes the CMN-ShuffleNet model to classify the lung cancer based on the extracted features. The effectiveness of the CMN-ShuffleNet model is confirmed via extensive statistical analysis that proves its superior performance in lung cancer classification with significant enhancements in metrics like accuracy, F1-score, precision, and recall compared to conventional techniques. For lung cancer classification LUNA16 dataset was used. The CMN-ShuffleNet approach has the highest accuracy rate of 95.8%. The CMN-ShuffleNet method achieved a sensitivity of 0.957 with 90% training data, indicating that it is a successful method for classifying lung cancer. The mRRB-SegNet attained the highest dice score of 0.926.