Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images

Mohan, Hari; Chatterjee, Kalyan

doi:10.1016/j.mlwa.2020.100004

Cited by 41 publications

(21 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ens M Ens are obtained using majority voting and weighted majority voting rules as in Equations ( 5) and (6).…”

Section: Ensmentioning

confidence: 99%

“…The problem with machine learning approaches is the requirement of human-engineered features. In the last decade, deep learning approaches, such as convolutional neural networks (CNN) became popular due to their ability with regard to automatic feature extraction [6][7][8][9], and have been extensively used in research [10][11][12][13]. Dorj et al [14] worked on skin cancer classification using deep CNN.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

et al. 2021

View full text Add to dashboard Cite

Skin cancer is a widespread disease associated with eight diagnostic classes. The diagnosis of multiple types of skin cancer is a challenging task for dermatologists due to the similarity of skin cancer classes in phenotype. The average accuracy of multiclass skin cancer diagnosis is 62% to 80%. Therefore, the classification of skin cancer using machine learning can be beneficial in the diagnosis and treatment of the patients. Several researchers developed skin cancer classification models for binary class but could not extend the research to multiclass classification with better performance ratios. We have developed deep learning-based ensemble classification models for multiclass skin cancer classification. Experimental results proved that the individual deep learners perform better for skin cancer classification, but still the development of ensemble is a meaningful approach since it enhances the classification accuracy. Results show that the accuracy of individual learners of ResNet, InceptionV3, DenseNet, InceptionResNetV2, and VGG-19 are 72%, 91%, 91.4%, 91.7% and 91.8%, respectively. The accuracy of proposed majority voting and weighted majority voting ensemble models are 98% and 98.6%, respectively. The accuracy of proposed ensemble models is higher than the individual deep learners and the dermatologists’ diagnosis accuracy. The proposed ensemble models are compared with the recently developed skin cancer classification approaches. The results show that the proposed ensemble models outperform recently developed multiclass skin cancer classification models.

show abstract

“…Ens M Ens are obtained using majority voting and weighted majority voting rules as in Equations ( 5) and (6).…”

Section: Ensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The proposed binary model has achieved an accuracy of 98.7%, sensitivity of 100%, and specificity of 98.3%, and the three-class model achieved an accuracy of 98.3%, a sensitivity of 99.3%, and specificity of 98.1% for detecting COVID-19. Rai et al [18] presented the novel deep neural network model with minimum layers and fewer complex to build on U-Net for detecting tumors. This work includes categorizing the brain MR images in regular or irregular classes of 253 high pixel images.…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Study on Augmented Analytics Using Deep Learning Techniques

Anusha

Kiruthika

2021

Smart Innovation, Systems and Technologies

View full text Add to dashboard Cite

Image augmentation is the most recognized type of data augmentation and intrinsic development for transforming image diversities in the training dataset that belongs to a similar class as the novel image. In the area of image augmentation handling, a collection of operations is shifting, flipping, zooming, cropping, rotation, and transformation in color space. A wide range of applications frequently used the aspects of deep learning are industry, science, and government domain, namely adaptive testing, image classification, computer vision, object detection, and face recognition and has achieved substantial development and accomplishment of deep learning. This study concentrates on the most important challenges present in the image estimation level that have a significant effect on dimension reduction, pooling, and edge detection. The deep learning methods involved here are convolution neural network (CNN), generative adversarial network (GAN), and deep convolution neural network (DCNN). Finally, a comparative study has performed a massive literature survey on various deep learning models.

show abstract

“…The development of CNN layers has provided important gains in the classification of images. In this study, it was aimed to use deep learning models with high performance rates by using large amount of data for the diagnosis of pneumonia [6].…”

Section: Introductionmentioning

confidence: 99%

Classification of Pneumonia Cell Images Using Improved ResNet50 Model

Çınar¹,

Yıldırım²,

Eroğlu³

2021

View full text Add to dashboard Cite

Pneumonia is a disease caused by inflammation of the lung tissue that is transmitted by various means, primarily bacteria. Early and accurate diagnosis is important in reducing the morbidity and mortality of the disease. The primary imaging method used for the diagnosis of pneumonia is lung x-ray. While typical imaging findings of pneumonia may be present on lung imaging, nonspecific images may be present. In addition, many health units may not have qualified personnel to perform this procedure or there may be errors in diagnoses made by traditional methods. For this reason, computer systems can be used to prevent error rates that may occur in traditional methods. Many methods have been developed to train data sets. In this article, a new model has been developed based on the layers of the ResNet50. The developed model was compared with the architectures InceptionV3, AlexNet, GoogleNet, ResNet50 and DenseNet201. In the developed model, the maximum accuracy rate was achieved as 97.22%. The model developed was followed by DenseNet201, ResNet50, InceptionV3, GoogleNet and AlexNet, respectively, according to their accuracy. With these developed models, the diagnosis of pneumonia can be made early and accurately, and the treatment management of the patient will be determined quickly.

show abstract

Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images

Cited by 41 publications

References 14 publications

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

A Comparative Study on Augmented Analytics Using Deep Learning Techniques

Classification of Pneumonia Cell Images Using Improved ResNet50 Model

Contact Info

Product

Resources

About