Histopathological analysis of whole-slide images (WSI) is the standard method for diagnosing lung cancer and classifying it into different types and subtypes, typically performed by specialized pathologists. However, manual examination is both time-consuming and prone to human error Automating the analysis is essential for enhancing diagnostic accuracy, but it requires significant computational power. This study focuses on automating the analysis of WSI for Lung Adenocarcinoma (LUAD) to categorize five distinct growth patterns (GP): Acinar, Lepidic, Papillary, Micro-Papillary, and Solid. WSIs are typically divided into smaller patch images to manage computational load, but identifying these patterns at the patch level presents considerable challenges. A novel approach using the Overlaid Residual Network Classifier (OL-RNC) has been proposed to address this issue. The OL-RNC operates in two phases: (1) identifying GP through advanced signal processing methods and (2) classifying the patterns using Deep Neural Networks (DNN). The OL-RNC demonstrates optimal patch identification for the following growth patterns: I. Solid (n = 5111; p = 105, 2.1%); II. Papillary (n = 10,221; p = 1286, 25.8%); III. Lepidic (n = 92,115; p = 2404, 48.1%); and IV. Acinar (n = 15,610; p = 1205, 24.1%), where n refers to the number of cohorts and p to the predominant patches. When compared to the baseline ResNet-50 method, OL-RNC outperforms it by achieving 2.21% higher average accuracy on the 20×UHCW dataset and 8.21% higher accuracy on the 40UHCW dataset. Overall, the classification accuracy for the five GPs is 95.21%, surpassing the accuracy of previous approaches. Beyond accuracy, this study integrates classical image processing with residual learning to achieve reliable patch-level diagnosis, enhance early detection, and facilitate data-driven decision-making in healthcare.