MeVs-deep CNN: optimized deep learning model for efficient lung cancer classification

Abstract

Lung cancer is a dangerous condition that impacts many people. The type and location of cancer are critical factors in determining the appropriate medical treatment. Early identification of cancer cells can save numerous lives, making the development of automated detection techniques essential. Although many methods have been proposed by researchers over the years, achieving high prediction accuracy remains a persistent challenge. Addressing this issue, this research employs Memory-Enabled Vulture Search Optimization based on Deep Convolutional Neural Networks (MeVs-deep CNN) to develop an autonomous, accurate lung cancer categorization system. The data is initially gathered from the PET/CT dataset and preprocessed using the Non-Local Means (NL-Means) approach. The proposed MeVs optimization approach is then used to segment the data. The feature extraction process incorporates statistical, texture, and intensity-based features and Resnet-101-based features, resulting in the creation of the final feature vector for cancer classification and the multi-level standardized convolutional fusion model. Subsequently, the MeVs-deep CNN leverages the MeVs optimization technique to automatically classify lung cancer. The key contribution of the research is the MeVs optimization, which effectively adjusts the classifier's parameters using the fitness function. The output is evaluated using metrics such as accuracy, sensitivity, specificity, AUC, and loss function. The efficiency of the MeVs-deep CNN is demonstrated through these metrics, achieving values of 97.08%, 97.93%, 96.42%, 95.88%, and 2.92% for training phase; 95.78%, 95.34%, 96.42%, 93.48%, and 4.22% for testing percentage; 96.33%, 95.20%, 97.65%, 94.83%, and 3.67% for k-fold train data; and 94.16%, 95.20%, 93.30%, 91.66%, and 5.84% for k-fold test data. These results demonstrate the effectiveness of the research.