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AbstractLung Cancer is a major cancer in the world and specifically India. Histopathological examination of tumorous tissue biopsy is the gold standard method used to clinically identify the type, sub-type, and stage of cancer. Two of the most prevalent forms of lung cancer: Adenocarcinoma & Squamous Cell Carcinoma account for nearly 80% of all lung cancer cases, which makes classifying the two subtypes of high importance. Proposed in this study is a data pre-processing pipeline for the H&E-stained lung biopsy images along with a customized EfficientNetB3-based Convolutional Neural Network employing spatial attention, trained on a public three-class lung cancer histopathological image dataset. The pre-processing pipeline employed before training, validation and testing helps enhance features of the histopathological images and removes biases due to stain variations for increased model robustness. The usage of a pre-trained CNN helps the deep learning model generalize better with the pre-trained weights, while the attention mechanism On three-fold validation, the classifier bagged accuracies of 0.9943 ± 0.0012 and 0.9947 ± 0.0018 and combined F1-Scores of 0.9942 ± 0.0042 and 0.9833 ± 0.0216 over the validation and testing data respectively. The high performance of the model combined with its computational efficiency could enable easy deployment of our model without necessitating infrastructure overhaul.