Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support

Harangi, Balázs; Baran, Ágnes; Hajdú, András

doi:10.1016/j.bspc.2020.102041

Cited by 50 publications

(26 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This innovative application has achieved excellent classification performance over conventional intelligent methods [24]- [26]. Esteva et al [24] demonstrated classification of skin lesions using a single convolutional neural network (CNN), trained end-to-end from images directly, using only pixels and disease labels as inputs, and it achieved performance on identification of the deadliest skin cancer and most common cancers; Gessert et al [25] proposed a patch-based attention architecture that provides global context between small and high-resolution patches with a novel diagnosis guided loss weighting method, outperformed previous methods and improves the mean sensitivity by 7%; Harangi et al [26] designed a deep convolutional neural network framework to classify dermoscopy images into seven classes, using GoogLeNet Inception-v3 and achieving remarkable improvement of 7%; Hosny et al [23] ing obstacles for the model's expansibility and efficiency, because it is necessary to convene professional doctors with costing much time to generate massive annotations. For the sake of relaxing this inconvenience, many exploitations [13], [14], [27]- [30] have been investigated to apply transfer learning into skin lesion classification.…”

Section: A Supervised Methods In Skin Lesion Classificationmentioning

confidence: 99%

Skin Lesion Classification by Multi-View Filtered Transfer Learning

et al. 2021

View full text Add to dashboard Cite

Skin cancer is one of the most deadly cancer types with considerable number of patients.Image analysis has largely improved the automated diagnosis accuracy for malignant melanoma and other pigmented skin lesions, compared to unaided visual examination. Recent popular solution for automated skin lesion classification is using deep neural networks, trained from large amounts of professional annotated data, but that largely limits the model's scalability. This paper exploits transfer learning for skin lesion classification task with the help of labeled data from another domain (source), and proposes a multi-view filtered transfer learning network to strongly represent discriminative information from different image views with reasonable weighing strategy. This method also evaluates the importance for each source images, which can learn useful knowledge with neglecting negative samples from source domain. The extensive skin lesion classification experiments demonstrate our method can effectively solve Melanoma and Seborrheic Keratosis classification tasks with outstanding extensibility, and the discussion of the major components also testifies the improvements of our proposed multi-view filtered transfer learning approach.

show abstract

Section: A Supervised Methods In Skin Lesion Classificationmentioning

confidence: 99%

Skin Lesion Classification by Multi-View Filtered Transfer Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The findings indicated that CNN Xception obtained a superior accuracy rate at 89%. A DCNN architecture for binary classification supporting multiple classes was proposed in [70] that offers greater outcome reliability in significant probabilities. An identical CNN architecture (GoofgLeNet Inception-v3) trained classification of multiple and binary classes concurrently.…”

Section: Deep Learning With Transfer Learning and Image Augmentationmentioning

confidence: 99%

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Saeed

Zeebaree

2021

JASTT

View full text Add to dashboard Cite

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

show abstract

“…Afterwards, the result (feature maps) at lth convolutional layer will be activated by an activation function to get non-linear features. In this model, the ReLu [29] activation was selected by the elite, which inverses the input x from negative to positive and keeps the positive values, as in Equation (15). The max(0, x) indicates ReLu function, F(x) is the ReLu output.…”

Section: Multimodal Fusionmentioning

confidence: 99%

“…The model accuracy was 0.97 on patient-level. Harangi et al [15] employed the deep GoogleNet Inception for classifying dermoscopy images. The accuracy reached 0.677.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

et al. 2021

View full text Add to dashboard Cite

Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

show abstract

Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support

Cited by 50 publications

References 12 publications

Skin Lesion Classification by Multi-View Filtered Transfer Learning

Skin Lesion Classification by Multi-View Filtered Transfer Learning

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

Contact Info

Product

Resources

About