2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 420-433, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Paparao Mekala 1 * , Surendiran B. 2
Doi: https://doi.org/10.54216/JISIoT.180229
One of the most dangerous and deadly illnesses that people can face in their lives is cancer. Among all cancers, skin cancer is one of the most damaging, hazardous, and potentially fatal to a person's life. If not detected it and treated initially, it will extend to other body parts soon and lead to the deadliest situation. It will spread quickly when the skin tissue areas are exposed to sunlight, mostly because skin cells in the designated location develop quickly. An automated skin tumor recognition system is the main requirement in order to detect skin cancer early, minimize time and effort, and save human lives. The most popular and successful methods for classifying skin cancer are the techniques of image processing and deep learning models. So, there is a need for an automated healthcare system to detect and classify skin lesions. We proposed a CNN model for classifying skin tumor images in our work. We have trained CNN models like AlexNet, VGG16, ResNet50, and Inceptionv3 using transfer learning techniques and observed the performance accuracies of all the models. The dataset used in our work contains two types of benign and melanoma skin tumor images, which are classified into two kinds through the Convolution Neural Network models. We used preprocessing techniques to clean our data, and data augmentation was also used to generate more data. As we know, deep learning models need more data to train and test the models. In all our model implementations, we have used all the features from the image while training the models for classification. Finally, we used the transfer learning techniques in our implementation models to improve the accuracy of each Image classification model. We trained the three models with different optimizers: Adam, Adadelta, and SGD. The proposed model (Modified AlexNet) provides better results, with approximately 96.75% for Training accuracy, 94.43% for Validation accuracy, and 94.11% for Testing Accuracy. The proposed model's performance results are compared with the state-of-the-art models like AlexNet, InceptionV3, VGG16, and ResNet50.
CNN , Skin Tumor , ResNet50 , VGG16 , InceptionV3 , Transfer learning , Adadelta, Adam , SGD
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