期刊
IEEE ACCESS
卷 11, 期 -, 页码 10336-10354出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3240443
关键词
Cancer; Deep learning; Convolutional neural networks; Brain modeling; Tumors; Transfer learning; Optimization; Bayes methods; Wireless networks; convolutional neural network (CNN); pretrained models; Bayesian optimization; transfer learning; learning without forgetting; VGG16; VGG19; DenseNet; mobile net
Cancer is the second leading cause of death globally, with one in six deaths attributed to it. Early detection improves the chances of survival, and the use of Artificial Intelligence (AI) for automated cancer detection can help evaluate more cases in less time.
Cancer is the second biggest cause of death worldwide, accounting for one of every six deaths. On the other hand, early detection of the disease significantly improves the chances of survival. The use of Artificial Intelligence (AI) to automate cancer detection might allow us to evaluate more cases in less time. In this research, AI-based deep learning models are proposed to classify the images of eight kinds of cancer, such as lung, brain, breast, and cervical cancer. This work evaluates the deep learning models, namely Convolutional Neural Networks (CNN), against classifying images with cancer traits. Pre-trained CNN variants such as MobileNet, VGGNet, and DenseNet are employed to transfer the knowledge they learned with the ImageNet dataset to detect different kinds of cancer cells. We use Bayesian Optimization to find the suitable values for the hyperparameters. However, transfer learning could make it so that models can no longer classify the datasets they were initially trained. So, we use Learning without Forgetting (LwF), which trains the network using only new task data while keeping the network's original abilities. The results of the experiments show that the proposed models based on transfer learning are more accurate than the current state-of-the-art techniques. We also show that LwF can better classify both new datasets and datasets that have been trained before.
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