Authors : Nitu Saha; Rituparna Mondal; Arunima Banerjee; Rupa Debnath; Siddhartha Chatterjee

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/35nznu6y

DOI : https://doi.org/10.38124/ijisrt/25jun1801

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Abstract : Lung cancer is a leading cause of cancer-related death globally, necessitating innovative diagnostic methods to enhance early detection and improve treatment effectiveness. This study presents "Advanced DeepLungCareNet," an enhanced deep learning framework designed to predict and classify lung cancer from medical imaging data with greater accuracy and reliability. The approach improves diagnostic efficacy by employing convolutional neural networks (CNNs) and incorporating sophisticated image processing algorithms. The study utilized the IQ-OTH/NCCD Lung Cancer Dataset from Kaggle, which includes a diverse collection of annotated medical images, such as computed tomography (CT) scans and X-rays. Data preprocessing included normalization, augmentation, and segmentation to improve input quality for the neural network. The model architecture has been refined with deeper convolutional layers, optimized pooling techniques, and sophisticated feature extraction algorithms, enabling the detection of minute anomalies and patterns in the imaging data. The performance evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, illustrate the superiority of "Advanced DeepLungCareNet" over existing state-of-the-art models. The framework achieved exceptional sensitivity and specificity, reducing false positives and false negatives, which is crucial for clinical reliability. The model demonstrated remarkable accuracy in detecting lung cancer from CT scans, making it a valuable tool for assisting healthcare professionals in early diagnosis. This study emphasizes the transformative potential of "Advanced DeepLungCareNet" in clinical environments, offering a robust solution for the early diagnosis and risk evaluation of lung cancer. Future attempts will focus on integrating multi-modal datasets, incorporating real-world clinical data, and exploring transfer learning approaches to enhance and validate the model's effectiveness across various healthcare situations.

Keywords : Advanced Deeplungcarenet, Lung Cancer, Machine Learning, Medical Imaging, Deep Learning, Convolutional Neural Networks (CNN), Resnet50, Computed Tomography (CT) Scans, Early Cancer Detection, Grad-CAM, Diagnostic Accuracy, Privacy in AI, IQ-OTH/NCCD Lung Cancer Dataset, Adam Optimizer.

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