Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

Moataz, Laila; Salama, Gouda I.; ElAzeem, Mohamed H. Abd

doi:10.1088/1742-6596/2128/1/012013

Cited by 16 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A huge quantity of dataset is essential to attain the best accuracy in the DL. The augmentation of data is done with various transformation techniques [ 33 , 34 , 35 ] like rotation, flipping, and brightening in sequence as shown in Figure 3 . For this, the input image is rotated 90 degrees in a clockwise direction.…”

Section: Proposed Framework Modelmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

View full text Add to dashboard Cite

Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers.

show abstract

Section: Proposed Framework Modelmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For image classification, we used the xception model as the deep convolutional neural network with pre-trained weights [4]. In a previous study, this model was successfully deployed for a similar classification task on skin lesions and was found to be efficient and reliable [5]. For training, we used pre-trained weights based on the imagenet dataset.…”

Section: Methodsmentioning

confidence: 99%

Automatic Wound Type Classification with Convolutional Neural Networks

Malihi

Hüsers

Richter

et al. 2022

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations. Here, artificial intelligence technology may support clinicians. In this study, we explore the performance of a deep convolutional neural network to classify diabetic foot and venous leg ulcers using wound images. We trained a convolutional neural network on 863 cropped wound images. Using a hold-out test set with 80 images, the model yielded an F1-score of 0.85 on the cropped and 0.70 on the full images. This study shows promising results. However, the model must be extended in terms of wound images and wound types for application in clinical practice.

show abstract

“…In [20] the authors presented a pre-trained Xception model with a method fine-tuned to classify skin cancer by adding a series of layers after the base layer of the Xception model and retraining the model weights. The input images are resized to 224 x 224 pixels.…”

Section: • Transfer Learning-based Approachesmentioning

confidence: 99%

A New Approach using Deep Learning and Reinforcement Learning in HealthCare

Yousra¹,

Abdelhakim²,

Mohamed³

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Nowadays, skin cancer is one of the most important problems faced by the world, due especially to the rapid development of skin cells and excessive exposure to UV rays. Therefore, early detection at an early stage employing advanced automated systems based on AI algorithms plays a major job in order to effectively identifying and detecting the disease, reducing patient health and financial burdens, and stopping its spread in the skin. In this context, several early skin cancer detection approaches and models have been presented throughout the last few decades to improve the rate of skin cancer detection using dermoscopic images. This work proposed a model that can help dermatologists to know and detect skin cancer in just a few seconds. This model combined the merits of two major artificial intelligence algorithms: Deep Learning and Reinforcement Learning following the great success we achieved in the classification and recognition of images and especially in the medical sector. This research included four main steps. Firstly, the pre-processing techniques were applied to improve the accuracy, quality, and consistency of a dataset. The input dermoscopic images were obtained from the HAM10000 database. Then, the watershed algorithm was used for the segmentation process performed to extract the affected area. After that, the deep convolutional neural network (CNN) was utilized to classify the skin cancer into seven types: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma melanocytic nevi, melanoma vascular skin lesions. Finally, in regards to the reinforcement learning part, the Deep Q_Learning algorithm was utilized to train and retrain our model until we found the best result. The accuracy metric was utilized to evaluate the efficacy and performance of the proposed method, which achieved a high accuracy of 80%. Furthermore, the experimental results demonstrate how reinforcement learning can be effectively combined with deep learning for skin cancer classification tasks.

show abstract

Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

Cited by 16 publications

References 8 publications

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

Automatic Wound Type Classification with Convolutional Neural Networks

A New Approach using Deep Learning and Reinforcement Learning in HealthCare

Contact Info

Product

Resources

About