Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images

Anand, Vatsala; Gupta, Sheifali; Koundal, Deepika; Singh, Karamjeet

doi:10.1016/j.eswa.2022.119230

Cited by 136 publications

(25 citation statements)

References 18 publications

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“…To obtain more, and more varied images of brain tumors, the data augmentation technique is used with the existing images. The different data augmentation techniques [ 31 , 32 , 33 ] that are applied are vertical flipping and horizontal flipping. Figure 6 a displays the original sample of the brain tumor image, Figure 6 b displays the vertically flipped image, and Figure 6 c displays the horizontally flipped image.…”

Section: Proposed Weighted Average Ensemble Deep Learning Model Archi...mentioning

confidence: 99%

“… Samples of Brain MRI Images [ 31 ]: ( a ) Original Image, ( b ) Vertical Flip, ( c ) Horizontal Flip. …”

Section: Figurementioning

confidence: 99%

“…To obtain more, and more varied images of brain tumors, the data augmentation technique is used with the existing images. The different data augmentation techniques [31][32][33] that are applied are vertical flipping and horizontal flipping. Figure 6a To obtain more, and more varied images of brain tumors, the data augmentation technique is used with the existing images.…”

Section: Model 3: Classification Using Convolution Neural Network (Cn...mentioning

confidence: 99%

“…Figure 6a To obtain more, and more varied images of brain tumors, the data augmentation technique is used with the existing images. The different data augmentation techniques [31][32][33] that are applied are vertical flipping and horizontal flipping. Figure 6a In this section, the results are obtained using the CNN model with augmented images.…”

Section: Model 3: Classification Using Convolution Neural Network (Cn...mentioning

confidence: 99%

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Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Anand

Gupta

et al. 2023

Diagnostics

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Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain.

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Section: Proposed Weighted Average Ensemble Deep Learning Model Archi...mentioning

confidence: 99%

“… Samples of Brain MRI Images [ 31 ]: ( a ) Original Image, ( b ) Vertical Flip, ( c ) Horizontal Flip. …”

Section: Figurementioning

confidence: 99%

Section: Model 3: Classification Using Convolution Neural Network (Cn...mentioning

confidence: 99%

Section: Model 3: Classification Using Convolution Neural Network (Cn...mentioning

confidence: 99%

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Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Anand

Gupta

et al. 2023

Diagnostics

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“…Smartphones can be used to enable self-diagnosis of skin cancer using this method. Vatsala Anand, Sheifali Gupta, Deepika Koundal [7] proposed the modified U-Net model architecture for the dermoscopy image segmentation of skin lesions in order to accurately classify skin diseases. The dermoscopy images come from the 200-image PH2 dataset.…”

Section: IImentioning

confidence: 99%

Skin Lesion Segmentation and Classification Using Deep Learning

S¹,

T²,

V³

2023

IJRASET

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Classification of skin lesions has recently received a lot of attention. Due to the high degree of similarity between the skin lesions, physicians frequently take a long time to examine them. A deep learning-based automated classification system can help doctors identify the type of skin lesion and improve the patient's health. With the development of deep learning architecture, the classification of skin lesions has emerged as a popular area of research. In this research, we present a method to use segmentation and techniques like Averaging of Deep Learning Architectures and classify skin lesions. We evaluated the proposal using a large dataset: HAM 10000. Our numerical results using VGGNet and ResNet using good results on the mentioned dataset.

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Dermo‐Optimizer: Skin Lesion Classification Using Information‐Theoretic Deep Feature Fusion and Entropy‐Controlled Binary Bat Optimization

Akram,

Alsuhaibani,

Khan

et al. 2024

Int J Imaging Syst Tech

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Increases in the prevalence of melanoma, the most lethal form of skin cancer, have been observed over the last few decades. However, the likelihood of a longer life span for individuals is considerably improved with early detection of this malignant illness. Even though the field of computer vision has attained a certain level of success, there is still a degree of ambiguity that represents an unresolved research challenge. In the initial phase of this study, the primary objective is to improve the information derived from input features by combining multiple deep models with the proposed Information‐theoretic feature fusion method. Subsequently, in the second phase, the study aims to decrease the redundant and noisy information through down‐sampling using the proposed entropy‐controlled binary bat selection algorithm. The proposed methodology effectively maintains the integrity of the original feature space, resulting in the creation of highly distinctive feature information. In order to obtain the desired set of features, three contemporary deep models are employed via transfer learning: Inception‐Resnet V2, DenseNet‐201, and Nasnet Mobile. By combining feature fusion and selection techniques, we may effectively fuse a significant amount of information into the feature vector and subsequently remove any redundant feature information. The effectiveness of the proposed methodology is supported by an evaluation conducted on three well‐known dermoscopic datasets, specifically , ISIC‐2016, and ISIC‐2017. In order to validate the proposed approach, several performance indicators are taken into account, such as accuracy, sensitivity, specificity, false negative rate (FNR), false positive rate (FPR), and F1‐score. The accuracies obtained for all datasets utilizing the proposed methodology are 99.05%, 96.26%, and 95.71%, respectively.

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Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images

Cited by 136 publications

References 18 publications

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Skin Lesion Segmentation and Classification Using Deep Learning

Dermo‐Optimizer: Skin Lesion Classification Using Information‐Theoretic Deep Feature Fusion and Entropy‐Controlled Binary Bat Optimization

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