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Cancer is a deadly disease that has gained a reputation as a global health concern. Further, lung cancer has been widely reported as the most deadly cancer type globally, while colon cancer comes second. Meanwhile, early detection is one of the primary ways to prevent lung and colon cancer fatalities. To aid the early detection of lung and colon cancer, we propose a computer-aided diagnostic approach that employs a Deep Learning (DL) architecture to enhance the detection of these cancer types from Computed Tomography (CT) images of suspected body parts. Our experimental dataset (LC25000) contains 25000 CT images of benign and malignant lung and colon cancer tissues. We used weights from a pre-trained DL architecture for computer vision, EfficientNet, to build and train a lung and colon cancer detection model. EfficientNet is a Convolutional Neural Network architecture that scales all input dimensions such as depth, width, and resolution at the same time. Our research findings showed detection accuracies of 99.63%, 99.50%, and 99.72% for training, validation, and test sets, respectively.
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