Abstract
The paper presents a convolutional neural network (CNN) based approach to classify lung cancer and colon cancer image dataset using two popular optimization algorithms: Adam and RMSprop. The lung cancer and colon cancer are very common among cancer-related diseases as around 25% of cancer patients suffer from either one. Survival rate may be increased if we identify the cancer in early stages. Deep learning, a field of machine learning has revolutionized the field of medical science. In the present study, separate models are built for the lung cancer and colon cancer using CNN to predict the class of the disease more accurately. The image dataset taken was available in the form of histopathological images. Paper consists of four models lung cancer using Adam, lung cancer using RMSprop, colon cancer using Adam, colon cancer using RMSprop, and performance (accuracy) of Adam and RMSprop is compared. The results are particularly useful for cancer prediction from images and can be generalized to other image classification problems as well.
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Zafar, A., Nadeem, M. (2022). Performance Evaluation of 2D CNN Optimizers for Lung and Colon Cancer Image Classification. In: Goyal, V., Gupta, M., Mirjalili, S., Trivedi, A. (eds) Proceedings of International Conference on Communication and Artificial Intelligence. Lecture Notes in Networks and Systems, vol 435. Springer, Singapore. https://doi.org/10.1007/978-981-19-0976-4_42
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