Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

Automated diagnosis of skin cancer can be easily achieved only by effective segmentation of skin lesion. But this is a highly challenging task due to the presence of intensity variations in the images of skin lesions. The authors here, have presented a histogram analysis based fuzzy C mean threshold technique to overcome the drawbacks. This not only reduces the computational complexity but also unifies advantages of soft and hard threshold algorithms. Calculation of threshold values even the presence of abrupt intensity variations is simplified. Segmentation of skin lesions is easily achieved, in a more efficient way in the following algorithm. The experimental verification here is done on a large set of skin lesion images containing every possible artifacts which highly contributes to reversed segmentation outputs. This algorithm efficiency was measured based on a comparison with other prominent threshold methods. This approach has performed reasonably well and can be implemented in the expert skin cancer diagnostic systems

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Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

Oncology ◽

10.4018/978-1-5225-0549-5.ch009 ◽

2017 ◽

pp. 302-309

Author(s):

T. Y. Satheesha ◽

D. Sathyanarayana ◽

M. N. Giri Prasad

Keyword(s):

Computational Complexity ◽

Skin Cancer ◽

Skin Lesion ◽

Skin Lesions ◽

Large Set ◽

Diagnostic Systems ◽

Automated Diagnosis ◽

Algorithm Efficiency ◽

Threshold Algorithms ◽

Threshold Technique

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Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

Computational Intelligence and Neuroscience ◽

10.1155/2021/9409508 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Cheng-Hong Yang ◽

Jai-Hong Ren ◽

Hsiu-Chen Huang ◽

Li-Yeh Chuang ◽

Po-Yin Chang

Keyword(s):

Image Segmentation ◽

Skin Cancer ◽

Skin Lesion ◽

Survival Rates ◽

Skin Lesions ◽

Manual Segmentation ◽

Cure Rate ◽

Dice Coefficient ◽

Skin Imaging ◽

Low Efficiency

Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.

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Remote Diagnosis and Triaging Model for Skin Cancer Using EfficientNet and Extreme Gradient Boosting

Complexity ◽

10.1155/2021/5591614 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Irfan Ullah Khan ◽

Nida Aslam ◽

Talha Anwar ◽

Sumayh S. Aljameel ◽

Mohib Ullah ◽

...

Keyword(s):

Skin Cancer ◽

Skin Lesion ◽

Clinical Data ◽

Cancer Diagnosis ◽

Gradient Boosting ◽

Automated Diagnosis ◽

Data Set ◽

Diagnosis System ◽

Extreme Gradient Boosting ◽

The Impact

Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble-learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD-UFES-20 data set consisting of six unbalanced categories of skin cancer. To overcome the data imbalance, we used data augmentation. Experiments were conducted using skin lesion merely and the combination of skin lesion and clinical data. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. Moreover, the proposed model outperformed the results achieved by the previous study for the PAD-UFES-20 data set with an accuracy of 0.78, precision of 0.89, recall of 0.86, and F1 of 0.88. In conclusion, the study provides an improved automated diagnosis system to aid the healthcare professional and patients for skin cancer diagnosis and remote triaging.

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The Study of Usefulness of a Set of Fractal Parameters to Build Classes of Disease Units Based on Images of Pigmented Skin Lesions

Diagnostics ◽

10.3390/diagnostics11101773 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1773

Author(s):

Monika Styła ◽

Tomasz Giżewski

Keyword(s):

Skin Lesion ◽

Fractal Analysis ◽

Skin Lesions ◽

Diagnostic Systems ◽

Kohonen Network ◽

Network Learning ◽

Fractal Parameters ◽

Fully Automatic ◽

Pigmented Skin Lesions

Dermatoscopic images are also increasingly used to train artificial neural networks for the future to provide fully automatic diagnostic systems capable of determining the type of pigmented skin lesion. Therefore, fractal analysis was used in this study to measure the irregularity of pigmented skin lesion surfaces. This paper presents selected results from individual stages of preliminary processing of the dermatoscopic image on pigmented skin lesion, in which fractal analysis was used and referred to the effectiveness of classification by fuzzy or statistical methods. Classification of the first unsupervised stage was performed using the method of analysis of scatter graphs and the fuzzy method using the Kohonen network. The results of the Kohonen network learning process with an input vector consisting of eight elements prove that neuronal activation requires a larger learning set with greater differentiation. For the same training conditions, the final results are at a higher level and can be classified as weaker. Statistics of factor analysis were proposed, allowing for the reduction in variables, and the directions of further studies were indicated.

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Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

Symmetry ◽

10.3390/sym11060790 ◽

2019 ◽

Vol 11 (6) ◽

pp. 790 ◽

Cited By ~ 8

Author(s):

Upender Kalwa ◽

Christopher Legner ◽

Taejoon Kong ◽

Santosh Pandey

Keyword(s):

Skin Cancer ◽

Performance Metrics ◽

Sampling Technique ◽

Smartphone Application ◽

Skin Lesions ◽

Cancer Diagnostics ◽

Support Vector ◽

Diagnostic Systems ◽

Image Capture ◽

Public Datasets

Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

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Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/9998379 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Fawaz Waselallah Alsaade ◽

Theyazn H. H. Aldhyani ◽

Mosleh Hmoud Al-Adhaileh

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Skin Cancer ◽

Skin Diseases ◽

Skin Lesions ◽

Machine Learning Algorithms ◽

Contour Method ◽

Ann Model ◽

Diagnostic Systems ◽

Feature Based

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F -score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

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Real-Time Mobile-Phone-Aided Melanoma Skin Lesion Detection Using Triangulation Technique

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.2020070102 ◽

2020 ◽

Vol 11 (3) ◽

pp. 9-31

Author(s):

Kumud Tiwari ◽

Sachin Kumar ◽

R. K. Tiwari

Keyword(s):

Skin Cancer ◽

Skin Lesion ◽

Cancer Detection ◽

Mobile Application ◽

Laboratory Testing ◽

Skin Lesions ◽

Lesion Detection ◽

Detection Accuracy ◽

Skin Cancer Detection ◽

Melanoma Skin

Melanoma is a harmful disease among all types of skin cancer. Genetic factors and the exposure of UV rays causes melanoma skin lesions. Early diagnosis is important to identify malignant melanomas to improve the patient prognosis. A biopsy is a traditional method which is painful and invasive when used for skin cancer detection. This method requires laboratory testing which is not very efficient and time-consuming to detect skin lesions. To solve the above issue, a computer aided diagnosis (CAD) for skin lesion detection is needed. In this article, we have developed a mobile application with the capabilities to segment skin lesions in dermoscopy images using a triangulation method and categorize them into malignant or bengin lesions through a supervised method which is convolution neural network (CNN). This mobile application will make the skin cancer detection non-invasive which does not require any laboratory testing, making the detection less time consuming and inexpensive with a detection accuracy of 81%.

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Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach

Applied Sciences ◽

10.3390/app10093045 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3045 ◽

Cited By ~ 2

Author(s):

Maria Rizzi ◽

Cataldo Guaragnella

Keyword(s):

Skin Lesion ◽

Medical Personnel ◽

Skin Lesions ◽

Lesion Segmentation ◽

Diagnostic Systems ◽

Low Contrast ◽

Redundant Data ◽

Computer Aided ◽

Bit Plane ◽

Value Decomposition

The establishment of automatic diagnostic systems able to detect and classify skin lesions at the initial stage are getting really relevant and effective in providing support for medical personnel during clinical assessment. Image segmentation has a determinant part in computer-aided skin lesion diagnosis pipeline because it makes possible to extract and highlight information on lesion contour texture as, for example, skewness and area unevenness. However, artifacts, low contrast, indistinct boundaries, and different shapes and areas contribute to make skin lesion segmentation a challenging task. In this paper, a fully automatic computer-aided system for skin lesion segmentation in dermoscopic images is indicated. Adopting this method, noise and artifacts are initially reduced by the singular value decomposition; afterward lesion decomposition into a frame of bit-plane layers is performed. A specific procedure is implemented for redundant data reduction using simple Boolean operators. Since lesion and background are rarely homogeneous regions, the obtained segmentation region could contain some disjointed areas classified as lesion. To obtain a single zone classified as lesion avoiding spurious pixels or holes inside the image under test, mathematical morphological techniques are implemented. The performance obtained highlights the method validity.

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Skin Lesions Classification Using Deep Learning Techniques: Review

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v9i130210 ◽

2021 ◽

pp. 1-22

Author(s):

Omar Sedqi Kareem ◽

Adnan Mohsin Abdulazee ◽

Diyar Qader Zeebaree

Keyword(s):

Deep Learning ◽

Skin Cancer ◽

Skin Lesion ◽

Learning Algorithm ◽

Skin Lesions ◽

Disease Diagnosis ◽

Deep Convolutional Neural Networks ◽

Deep Learning Algorithm ◽

The Usa ◽

Effective Models

Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

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FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Sensors ◽

10.3390/s21155172 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5172

Author(s):

Yuying Dong ◽

Liejun Wang ◽

Shuli Cheng ◽

Yongming Li

Keyword(s):

Skin Lesion ◽

Real Life ◽

Good Deal ◽

Experimental Tests ◽

Skin Lesions ◽

Lesion Segmentation ◽

Attention Network ◽

Effective Feedback ◽

Public Datasets ◽

Learning Architectures

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

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