Fast fully automatic skin lesions segmentation probabilistic with Parzen window

Chagas, João Victor Souza das; Ivo, Roberto F.; Guimarães, Matheus T.; Rodrigues, Douglas de A.; Rebouças, Elizângela de S.; Filho, Pedro Pedrosa Rebouças

doi:10.1016/j.compmedimag.2020.101774

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…However, treatment is possible if it is detected in its early stages [6]. According to recent statistics, it was reported that 20% of skin cancer reached a point where survival is not possible due to the disease progression [7]. Worldwide, approximately 50,000 people die each year from skin cancer [7,8], which represents 0.7 of the death rate due to cancer [8].…”

Section: Introductionmentioning

confidence: 99%

“…According to recent statistics, it was reported that 20% of skin cancer reached a point where survival is not possible due to the disease progression [7]. Worldwide, approximately 50,000 people die each year from skin cancer [7,8], which represents 0.7 of the death rate due to cancer [8]. The estimated cost of treatment is approximately USD 30 million, which is prohibitive for treatment [5].…”

Section: Introductionmentioning

confidence: 99%

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On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Fraiwan

Faouri

2022

Sensors

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Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Fraiwan

Faouri

2022

Sensors

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“…ML-based method can segment skin lesions more effectively, which is generally encoded by developing feature extractor. Chagas et al [29] extracted the features of the skin lesion area using a sliding window. Ramya et al [30] addressed the complexities inherent in dermoscopy images by utilizing wavelet transforms.…”

Section: Traditional Skin Lesion Segmentation Methodsmentioning

confidence: 99%

“…Chagas et al. [29] extracted the features of the skin lesion area using a sliding window. Ramya et al.…”

Section: Related Workmentioning

confidence: 99%

Weakly supervised skin lesion segmentation based on spot‐seeds guided optimal regions

Al‐Huda

Yuan

Yao

et al. 2022

IET Image Processing

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Automatic skin lesion segmentation is the most critical and relevant task in computeraided skin cancer diagnosis. Methods based on convolutional neural networks (CNNs) are mainly used in current skin lesion segmentation. The requirement of huge pixellevel labels is a significant obstacle to achieve semantic segmentation of skin lesion by CNNs. In this paper, a novel weakly supervised framework for skin lesion segmentation is presented, which generates high-quality pixel-level annotations and optimizes the segmentation network. A hierarchical image segmentation algorithm can predict a boundary map for training images. Then, the optimal regions of candidate hierarchical levels are selected. Afterward, Superpixels-CRF built on the optimal regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, a segmentation network can be trained and segmentation masks can be predicted. To iteratively optimize the segmentation network, the predicted segmentation masks are refined and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework reduces the gap between weakly and fully supervised skin lesion segmentation methods, and achieves state-of-the-art performance while reducing human labeling efforts.

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“…As a result of ReLU, the vanishing and exploding gradient problem is significantly reduced in the history of neural networks. Compared to the standard nonlinear activation functions like sigmoid and tangent [47], it is proven to be the most efficient way to train large datasets in terms of time and cost. As a result of this nonlinearity, neurons are referred to as [34].…”

Section: Loss Functionsmentioning

confidence: 99%

A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification

Ugendhar

Illuri²,

Vulapula

et al. 2022

Mathematical Problems in Engineering

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Cybersecurity in information technology (IT) infrastructures is one of the most significant and complex issues of the digital era. Increases in network size and associated data have directly affected technological breakthroughs in the Internet and communication areas. Malware attacks are becoming increasingly sophisticated and hazardous as technology advances, making it difficult to detect an incursion. Detecting and mitigating these threats is a significant issue for standard analytic methods. Furthermore, the attackers use complex processes to remain undetected for an extended period. The changing nature and many cyberattacks require a quick, adaptable, and scalable defense system. For the most part, traditional machine learning-based intrusion detection relies on only one algorithm to identify intrusions, which has a low detection rate and cannot handle large amounts of data. To enhance the performance of intrusion detection systems, a new deep multilayer classification approach is developed. This approach comprises five modules: preprocessing, autoencoding, database, classification, and feedback. The classification module uses an autoencoder to decrease the number of dimensions in a reconstruction feature. Our method was tested against a benchmark dataset, NSL-KDD. Compared to other state-of-the-art intrusion detection systems, our methodology has a 96.7% accuracy.

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Fast fully automatic skin lesions segmentation probabilistic with Parzen window

Cited by 12 publications

References 19 publications

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Weakly supervised skin lesion segmentation based on spot‐seeds guided optimal regions

A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification

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