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

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.

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

“…In [92], various types of CNNs (ResNet, DenseNet, InceptionV3, VGG16) pretrained on ImageNet are implemented to evaluate their performance in the skin cancer diagnosis task. After selecting some features of the InceptionV3 and DenseNet architectures, a new architecture called DenseNet-II is built in which there are two parallel networks of convolutional layers.…”

Detect Melanoma Skin Cancer Using An Improved Deep Learning CNN Model With Improved Computational Costs

“…There have been heaps of DL based works which have given extraordinary results for melanoma detection. Few include, which implements a Densenet II model [9] and gives accurate results than many other transfer learning models on HAM 10000 dataset.…”

“…This architecture finds its basis from the DenseNet II [9] architecture. It has been cost efficient and fewer layers have been used in its implementation.…”

Skin cancer diagnosis using artificial intelligence methods: a review of the most recent research

“…They obtained 98.32% of accuracy. In 49 , the authors used a variety kind of CNNs, including VGG16, ResNet, InceptionV3, and DenseNet which had been trained on ImageNet, to evaluate how well they performed in the mission of diagnosing skin tumors.…”