Deep Learning Enhanced CNN with Bio-Inspired Techniques and BCE For Effective Lung Nodules Detection & Classification For Accurate Diagnosis

Objectives: To propose a new hybrid deep learning model to boost lung nodule detection and classification in combination with a bioinspired method for efficient hyper-parameter optimization to maximize true positive and nodule discrimination rates. Methods: In order to extract the intrinsic and complex features from different lung nodules, deep learning-based enhanced CNN (ECNN) is used. Segmentation masks are generated using Mask-RCNN to isolate and capture the ROIs, which serve as input to CNN. Prior to segmentation and extraction, data cleaning, transformation, and augmentation is done. Hyper-parameter optimization is done using a bio-inspired differential evolution method, which helps to identify the learning rate and number of layers to achieve optimal performance. We utilize BCE to quantify the performance of classification tasks. A large-scale CT-DICOM image dataset with 25,1135 images is used for this research work, collected from the cancer imaging archive, which has a clinically proven record of 355 instances with detailed image analysis, tumor location, and bounding boxes with a resolution of 512x512 pixels. 175794 images are used for training, and 75341 images are used for testing and validation. The performance of the new system is assessed using MATLAB, where results are compared with existing models such as SVM-WSS, GCPSO-PNN, and 3D-DLCNN models. Findings: The newly suggested ECNN with DE Bio-inspired model boosts the performance of lung nodule detection and classification with 94.7% accuracy, 93.8% sensitivity, 94.5% specificity, 93.4% F1 score, 92.4% dice coefficient, 0.1 Log Loss and AUC-ROC with 0.93 TPR and 0.07 FPR. Novelty: The novel method presents an advanced computational model using deep learning and bio-inspired algorithms for robust classification of lung nodules, which significantly improves the early diagnosis. This CAD model overcomes the limitations of the existing approaches SVM-WSS, GCPSO-PNN, and 3D-DLCNN in terms of segmentation, classification, error detection, and hyper-parameter tuning. Keywords: Lung Nodules, Deep Learning, Disease Classification, Image Processing, ECNN- DE Classifier, CT-DICOM Dataset