Comparison of the impacts of dermoscopy image augmentation methods on skin cancer classification and a new augmentation method with wavelet packets

Göçeri, Evgin

doi:10.1002/ima.22890

Cited by 39 publications

(2 citation statements)

References 44 publications

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“…As a potential future endeavour, a comparison between the performance of the proposed method and segmentation methods based on capsule networks could be explored, delving into their respective advantages. Lastly, it is well recognized that deep networks require ample data and various augmentation techniques have been applied to address issues and enhance the reliability and robustness of deep networks [49–51]. Therefore, as an extension of this work, evaluating the proposed method with an increased amount of data could be considered.…”

Section: Conculsion and Future Workmentioning

confidence: 99%

Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T

Xu,

Yu,

et al. 2024

IET Image Processing

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Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T.

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Section: Conculsion and Future Workmentioning

confidence: 99%

Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T

Xu,

Yu,

et al. 2024

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…However, the application of deep learning in this context is hindered by the availability of small or insufficiently annotated sample datasets [5]. This challenge is particularly relevant in the context of imbalanced datasets, where the unequal distribution of training examples can result in biased results favoring the majority class and high misclassification rates for the minority class [6]. Therefore, addressing the issue of imbalanced datasets becomes crucial for achieving accurate and reliable automated DR diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Ensembled Deep Convolutional Generative Adversarial Network for Grading Imbalanced Diabetic Retinopathy Recognition

Naz,

Nijhawan,

Ahuja

et al. 2023

IEEE Access

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Diabetic Retinopathy (DR) is one of the leading causes of blindness and vision loss worldwide. According to the International Diabetes Federation (IDF), approximately one-third of individuals with diabetes, equivalent to 32.2%, are affected by some form of DR. Due to the uneven data distribution, intra-class variance, and a dearth of ophthalmologists, DR diagnosis is considered challenging. In recent years, Convolutional Neural Networks (CNN) and supervised learning techniques have been potentially useful in computer vision applications. However, unsupervised CNN has received less attention. Moreover, it is more manageable to use synthetic images for model training with the recent advancements in graphics. Therefore, the proposed method combines the actual and augmented views using the Deep Convolutional Generative Adversarial Network (DCGAN) algorithm. The generated views are implemented to balance the minority class in the imbalanced dataset. Furthermore, a novel ensemble convolutional neural network algorithm named Different View Ensemble (DVE) that merges the weighted average prediction of CNN, CNN-i, and CNN+i algorithms has been proposed. The proposed algorithm is evaluated on the DDR and EyePACS datasets, and its performance is compared with K-Means, Fuzzy C-Means (FCM), and Autoencoder-based Deep Embedded Clustering Techniques (DEC).The results demonstrate the superiority of the proposed algorithm, achieving an accuracy rate of 97.4%, specificity of 99.6%, and sensitivity of 92.3%. The promising results underscore the potential impact of this methodology in enhancing the accuracy and reliability of automated diagnostic systems in the field of ophthalmology. Notably, the evaluation considers imbalanced data and a DCGAN-balanced dataset, where the proposed approach exhibits even better performance with balanced classes. INDEX TERMSDiabetic Retinopathy detection, Imbalance data, Ensembled GAN, Healthcare, Health risksFIGURE 1. Fundus Images and Abnormal DR Grades (a) Mild NPDR (b) Moderate NPDR (c) Severity NPDR (d) PDR [1]

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A Hybrid Convolutional Neural Network Model for the Classification of Multi‐Class Skin Cancer

Toprak,

Aruk

2024

Int J Imaging Syst Tech

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Skin cancer is a significant public health issue, making accurate and early diagnosis crucial. This study proposes a novel and efficient hybrid deep‐learning model for accurate skin cancer diagnosis. The model first employs DeepLabV3+ for precise segmentation of skin lesions in dermoscopic images. Feature extraction is then carried out using three pretrained models: MobileNetV2, EfficientNetB0, and DenseNet201 to ensure balanced performance and robust feature learning. These extracted features are then concatenated, and the ReliefF algorithm is employed to select the most relevant features. Finally, obtained features are classified into eight categories: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, melanocytic nevus, squamous cell carcinoma, and vascular lesion using the kNN algorithm. The proposed model achieves an F1 score of 93.49% and an accuracy of 94.42% on the ISIC‐2019 dataset, surpassing the best individual model, EfficientNetB0, by 1.20%. Furthermore, the evaluation of the PH2 dataset yielded an F1 score of 94.43% and an accuracy of 94.44%, confirming its generalizability. These findings signify the potential of the proposed model as an expedient, accurate, and valuable tool for early skin cancer detection. They also indicate combining different CNN models achieves superior results over the results obtained from individual models.

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Comparison of the impacts of dermoscopy image augmentation methods on skin cancer classification and a new augmentation method with wavelet packets

Cited by 39 publications

References 44 publications

Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T

Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T

Ensembled Deep Convolutional Generative Adversarial Network for Grading Imbalanced Diabetic Retinopathy Recognition

A Hybrid Convolutional Neural Network Model for the Classification of Multi‐Class Skin Cancer

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