DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection

Nancy Girdhar; Aparna Sinha; Shivang Gupta

doi:10.1007/s00500-022-07406-z

DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection

Soft comput. 2022 Aug 24:1-20. doi: 10.1007/s00500-022-07406-z. Online ahead of print.

Authors

Nancy Girdhar¹, Aparna Sinha^#², Shivang Gupta^#²

Affiliations

¹ School of Computer Science Engineering and Technology, Bennett University, Greater Noida, UP India.
² Amity School of Engineering and Technology, Amity University, Noida, UP India.

^# Contributed equally.

Abstract

Research in the field of medicine and relevant studies evince that melanoma is one of the deadliest cancers. It defines precisely that the condition develops due to uncontrolled growth of melanocytic cells. The current trends in any disease detection revolve around the usage of two main categories of models; these are general machine learning models and deep learning models. Further, the experimental analysis of melanoma has an additional requirement of visual records like dermatological scans or normal camera lens images. This further accentuates the need for a more accurate model for melanoma detection. In this work, we aim to achieve the same, primarily by the extensive usage of neural networks. Our objective is to propose a deep learning CNN framework-based model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture, activation functions applied, and the dimension of the input array. Models like Resnet, DenseNet, Inception, and VGG have proved to yield appreciable accuracy in melanoma detection. However, in most cases, the dataset was classified into malignant or benign classes only. The dataset used in our research provides seven lesions; these are melanocytic nevi, melanoma, benign keratosis, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma. Thus, through the HAM10000 dataset and various deep learning models, we diversified the precision factors as well as input qualities. The obtained results are highly propitious and establish its credibility.

Keywords: Deep learning; DenseNet; HAM10000; Lesions; Machine learning; Melanoma detection; ResNet; VGG.

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