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The study proposes combining Differential Augmentation (DA) with Convolutional Neural Networks (CNNs) to mitigate memory overfitting and improve generalization to unseen data. Targeted DA adjusts hue, brightness, saturation, and contrast to diversify training images. The approach is evaluated on multiple datasets, including IQ-OTH/NCCD, with Random Search hyperparameter tuning and performance comparisons against state-of-the-art CNNs and hybrid ensemble methods. Results show 98.78% accuracy, supported by Tukey’s HSD significance tests.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/optimizing-non-small-cell-lung-cancer-detection-with-convolutional-neural-networks-and-differential-augmentation/367438/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/optimizing-non-small-cell-lung-cancer-detection-with-convolutional-neural-networks-and-differential-augmentation/367438.png","ImageObject",300,407,{"name":92,"@type":93},"Maya Linwood","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the CNN + Differential Augmentation approach target?","Question",{"text":112,"@type":113},"It targets memory overfitting, which limits CNN generalization to unseen data. Differential Augmentation diversifies training samples to improve robustness.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is Differential Augmentation applied in the study?",{"text":117,"@type":113},"DA uses targeted image adjustments including hue, brightness, saturation, and contrast. These changes increase variability in the training set.",{"name":119,"@type":110,"acceptedAnswer":120},"How did the proposed model perform compared with existing methods?",{"text":121,"@type":113},"The CNN + DA model reached 98.78% accuracy, outperforming DenseNet, ResNet, EfficientNetB0, and hybrid ensemble approaches. Tukey’s HSD post-hoc tests further confirmed the superiority statistically.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},367438,1790195364,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962084928432,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nOptimizing non small cell  \nlung cancer detection with convolutional neural networks and differential augmentation  \nVahiduddin Shariff1􀀍, Chiranjeevi Paritala2 & Krishna MohanAnkala1  \nLung cancer remains one of the leading causes of cancer-related deaths worldwide, with early detection being critical to improving patient outcomes. Recent advancements in deep learning have shown promise in enhancing diagnostic accuracy, particularly through the use of Convolutional Neural Networks (CNNs). This study proposes the integration of Differential Augmentation (DA) with CNNsto address the critical challenge of memory overfitting, a limitation that hampers the generalization of models to unseen data. By introducing targeted augmentation strategies, such as adjustments in hue, brightness, saturation, and contrast, the CNN + DA model diversifies training data and enhancesits robustness. The research utilized multiple datasets, including the IQ-OTH/NCCD dataset, to evaluate the proposed model against existing state-of-the-art methods. Hyperparameter tuning was performed using Random Search to optimize parameters, further improving performance. The results revealed that the CNN + DA model achieved an accuracy of 98.78%, outperforming advanced models like DenseNet, ResNet, and EfficientNetB0, as well as hybrid approaches including ensemble models. Additionally, statistical analyses, including Tukey’s HSD post-hoc tests, confirmed the significance of the model’s superior performance. These findings suggest that the CNN + DA model effectively addresses the limitations of prior works by reducing overfitting and ensuring reliable generalization across diverse datasets. The study concludes that the novel CNN + DA architecture provides a robust, accurate, and computationally efficient framework for lung cancer detection, positioning itas a valuable tool for clinical applications and paving the way for future research in medical image diagnostics.  \nKeywords Lung Cancer prediction, Convolutional neural networks, Differential augmentation (DA), Memorization overfitting, Medical imaging, Diagnostic robustness, EfficientNetB0  \nInnovative lung cancer prognosis relies on cutting-edge machine learning algorithms applied to a wealth of patient data. By incorporating various elements like imaging, genetics, and medical records, this model demonstrates remarkable precision in anticipating potential occurrences. Optimizing early intervention and customizing treatments, it holds the potential to elevate patient outcomes in the challenging realm of lung cancer. Early detection is vital for successful treatment1 and goal is to authenticate our Lung Cancer Prediction CNN, initially trained on US screening data, through an autonomous European multicenter dataset2. This research explores early-stage lung cancer diagnosis using CNNs, aiming to enhance diagnostic accuracy and save lives by improving early detection through reliable machine learning models3. While CNN models have demonstrated high accuracy on controlled datasets, their applicability to real-world scenarios with diverse patient populations remains a critical challenge. Addressing this requires validation across multiple datasets and strategies to ensure robustness against unseen variations in imaging data. Introducing a method utilizing Convolutional Neural Networks (CNN) to categorize tumors in lung disease screening through tomography filters, harnessing spatial invariance for effective feature extraction4. Utilizing end-to-end learning CNN, this method automatically extracts self-learned features, comparing favorably with conventional approaches and traditional computeraided diagnosis systems5.  \n1Department of Computer Science and Engineering, UCEK, Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh-533003, India. 2Department of Computer Science and Engineering, Amrita Sai Institute of Scie","cbCaicsdqLqF14cZ","https://ap.wps.com/l/cbCaicsdqLqF14cZ","pdf",4276231,27,"English","# Optimization strategy for NSCLC detection\n## Differential Augmentation with CNNs\n## Datasets and model evaluation\n## Hyperparameter tuning and comparisons\n## Statistical significance testing\n## Conclusions and clinical implications","[{\"question\":\"What problem does the CNN + Differential Augmentation approach target?\",\"answer\":\"It targets memory overfitting, which limits CNN generalization to unseen data. Differential Augmentation diversifies training samples to improve robustness.\"},{\"question\":\"How is Differential Augmentation applied in the study?\",\"answer\":\"DA uses targeted image adjustments including hue, brightness, saturation, and contrast. These changes increase variability in the training set.\"},{\"question\":\"How did the proposed model perform compared with existing methods?\",\"answer\":\"The CNN + DA model reached 98.78% accuracy, outperforming DenseNet, ResNet, EfficientNetB0, and hybrid ensemble approaches. Tukey’s HSD post-hoc tests further confirmed the superiority statistically.\"}]","Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation | PDF",1790170797,68]