Abstract
Abstract: Utilising Fastai and PyTorch, a deep learning model for classifying lung cancer was created. The model, which is based on the ResNet-30 architecture, was trained using a dataset that included the classes "lung_aca," "lung_n," and "lung_scc." Rotation and scaling were added to the dataset as enhancements. The training and validation sets each had 30 samples, and the model attained a high level of accuracy. Loss plots were employed to display the outcomes and the Fastai library was utilised for simple model construction and training. The Fastai and PyTorch versions of this model have been modified for best performance, and it is a potent tool for precise lung cancer categorization.
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