A Modified Inception-v4 for Imbalanced Skin Cancer Classification Dataset

Emara, Taha; Afify, Heba M.; Ismail, Fatma Helmy; Hassanien, Aboul Ella

doi:10.1109/icces48960.2019.9068110

Cited by 22 publications

(13 citation statements)

References 21 publications

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“…For allowing transfer learning or reusing these models for image classification with different types, last layer that is called the fully-connected layer should be replaced with another fully-connected unit. Different pre-trained models are used, such as DenseNet [14], ResNet [15], Inception-v3 [16], Xception [17], GoogleNet [18], MobileNet [19], and VGG16 [20] networks. Each of these pre-trained models can be retrained using the same weights and parameters for classifying COVID-19 cases.…”

Section: B2 Pre-trained Modelsmentioning

confidence: 99%

COVID-19 Diagnosis Using X-ray Images Based on Convolutional Neural Networks

Shalaby

Saad

Shokair

et al. 2021

2021 International Conference on Electronic Engineering (ICEEM)

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Coronavirus (COVID-19) is considered as a viral disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Spreading of COVID-19 will continue to affect health and economics. Chest X-ray and CT imaging techniques are crucial for infected patients in the battle with COVID-19. Recently, Convolutional Neural Network has been considered as a type of deep learning tools, and it can be used for detecting diseases such as COVID-19. This paper introduces an efficient architecture for COVID-19 diagnosis from an X-ray dataset. The proposed architecture starts with image pre-processing using lung segmentation and image resizing. Deep feature extraction is performed using the proposed CNN model and different pre-trained models. The classification process is performed using either a Support Vector Machine (SVM) or a Softmax classifier. Simulation results prove that the proposed model can classify COVID-19 images with high accuracies of 98.7% and 98.5% for SVM and Softmax classifiers, respectively. The performance metrics are the processing time,

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Section: B2 Pre-trained Modelsmentioning

confidence: 99%

COVID-19 Diagnosis Using X-ray Images Based on Convolutional Neural Networks

Shalaby

Saad

Shokair

et al. 2021

2021 International Conference on Electronic Engineering (ICEEM)

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show abstract

“…For the utilization of deep learning approach in skin cancer classification, Emara et al [19] proposed the modified Inception-v4 architecture which was also a CNN architecture to classify skin lesion through imbalance HAM 10000 skin cancer dataset [20]. The enhancement of this work was employing the feature reuse using long residual connection to improve classification performance with the achievement of high accuracy, 94.70% but this pretrained DNN has the complex network with a huge number of parameters that may causes the limited classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian

Chin

Mat

Saleh

2021

IJ-AI

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Skin cancer is a widely spreading cause of mortality among the people specifically living on or near the equatorial belt. Early detection of skin cancer significantly improves the recovery prevalence and the chance of surviving. Without the assist of computer-aided decision (CAD) system, skin cancer classification is the challenging task for the dermatologist to differentiate the type of skin cancer and provide the suitable treatment. Recently, the development of machine learning and pretrained deep neural network (DNN) shows the tremendous performance in image classification task which also provide the promising performance in medical field. However, these machine learning methods cannot get the deep features from network flow which resulting in low accuracy and the pretrained DNN has the complex network with a huge number of parameters causes the limited classification accuracy. This paper focuses on the classification of skin cancer to identify whether it is basal cell carcinoma, melanoma or squamous cell carcinoma by using the development of hybrid convolutional neural network algorithm and autoregressive integrated moving average model (CNN-ARIMA). The CNNARIMA model was trained and found to produce the best accuracy of 92.25%.

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“…The analysis is done for RGB images in classifying skin cancer (Xia, Xu, and Nan 2017). in this study flower classification was done on Oxford -17 flowers and Oxford -102 flowers dataset using Inception V3 and it is reported that this technique is performing better compared with other methods (Emara et al 2019(Vijayashree Priyadarshini 2019;Ezhilarasan, Apoorva, and Ashok Vardhan 2019;…”

mentioning

confidence: 92%

Detection of Skin Cancer using Inception V3 And Inception V4 Convolutional Neural Network (CNN) For Accuracy Improvement

Nandini¹,

Puviarasi²

2021

revistageintec

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Aim:The main aim of this work is to measure the accuracy for automated detection of dermal cell images using the Convolutional Neural Network (CNN) algorithm. Materials and Methods:The skin images the dataset collected from International skin images collaboration (ISIC). In this framework 1200 images are used out of which (80%) are trained and (20%) are used for testing for the detection of skin cancer. 1200 images are used for group I (Inception V4) in comparison with Inception V3 and statistical analysis done using SPSS analysis. The sample size of two groups is calculated using G power with pretest power of 80 and alpha value 0.05 (error rate) with inputs 2400 (1200*2). Results: The inception V4 using CNN shows better results in mean Accuracy of 92.34±0.87 followed by inception V3 produces an accuracy of 90.34±0.13 with the significance value of <0.001. Conclusion: It is concluded that based on the execution analysis, the Inception V4 appears to be better accuracy compared with the Inception V3 algorithm.

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A Modified Inception-v4 for Imbalanced Skin Cancer Classification Dataset

Cited by 22 publications

References 21 publications

COVID-19 Diagnosis Using X-ray Images Based on Convolutional Neural Networks

COVID-19 Diagnosis Using X-ray Images Based on Convolutional Neural Networks

Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian

Detection of Skin Cancer using Inception V3 And Inception V4 Convolutional Neural Network (CNN) For Accuracy Improvement

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