Early Detection and Classification of Melanoma Skin Cancer

Alasadi, Abbas H. Hassin; Alsafy, Baidaa M.

doi:10.5815/ijitcs.2015.12.08

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…Finally modified c means clustering algorithm segments the skin lesion image. Abbas and Baidaa proposed a system consists of two phases of several stages [17]. First phase checks for malignant and benign cancer type and last phase recognizes malignant melanoma type.…”

Section: Literature Reviewmentioning

confidence: 99%

Skin Lesion Detection Using Fuzzy Approach and Classification with CNN

Dhar¹

2021

IJEM

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Skin lesion detection at early stage is very effective for patients. As a result, patients can get time for treatment. Moreover, this early detection helps a patient in the long-time survival. However, skin lesion detection from a dermoscopic images is not a general task. Due to inter and intra-observer variations in human interpretations, research on skin lesion detection from dermoscopic images become important. In this paper, we proposed a method to segment and detect lesion of skin from images. The proposed method is based on a set of rules of fuzzy logic approach. Firstly, a set of rules is applied on dermoscopic images. The output is then thresholded. Closing operation as a morphological tool is used on the thresholded image. Then area filtering takes a place which results in the desired output. With respect to different learning models, CNN shows better performance in classifying ISIC and Dermis-dermquest dataset. The system delivers a significant performance, which is remarkable and comparable.

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Section: Literature Reviewmentioning

confidence: 99%

Skin Lesion Detection Using Fuzzy Approach and Classification with CNN

Dhar¹

2021

IJEM

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“…At first the image preprocessing is done in 3 steps:-(i) Reduction of size to 384*216, (ii) Adjustment of brightness using histogram equalization, (iii) Median filtering of image. Median Filtering is an image filtering method in which each pixel value in an image is replaced with the median value of its neighboring pixels including itself [25]. Next, the segmentation of color image is done based on HSV color model, followed by morphological processing and area filtration respectively.…”

Section: Research On Road Traffic Sign Recognition Based On Video Imagementioning

confidence: 99%

Traffic Sign Detection and Recognition Model Using Support Vector Machine and Histogram of Oriented Gradient

Ahmed¹,

Rabbi²,

Rahman³

et al. 2021

IJITCS

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Traffic signs are symbols erected on the sides of roads that convey the road instructions to its users. These signs are essential in conveying the instructions related to the movement of traffic in the streets. Automation of driving is essential for efficient navigation free of human errors, which could otherwise lead to accidents and disorganized movement of vehicles in the streets. Traffic sign detection systems provide an important contribution to automation of driving, by helping in efficient navigation through relaying traffic sign instructions to the system users. However, most of the existing techniques have proposed approaches that are mostly capable of detection through static images only. Moreover, to the best of the author’s knowledge, there exists no approach that uses video frames. Therefore, this article proposes a unique automated approach for detection and recognition of Bangladeshi traffic signs from the video frames using Support Vector Machine and Histogram of Oriented Gradient. This system would be immensely useful in the implementation of automated driving systems in Bangladeshi streets. By detecting and recognizing the traffic signs in the streets, the automated driving systems in Bangladesh will be able to effectively navigate the streets. This approach classifies the Bangladeshi traffic signs using Support Vector Machine classifier on the basis of Histogram of Oriented Gradient property. Through image processing techniques such as binarization, contour detection and identifying similarity to circle etc., this article also proposes the actual detection mechanism of traffic signs from the video frames. The proposed approach detects and recognizes traffic signs with 100% precision, 95.83% recall and 96.15% accuracy after running it on 78 Bangladeshi traffic sign videos, which comprise 6 different kinds of Bangladeshi traffic signs. In addition, a public dataset for Bangladeshi traffic signs has been created that can be used for other research purposes.

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“…Skin cancer is among the most frequent cancers. It is the main cause of death worldwide [1]. Cancer has been brought on by environmental changes today [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of melanoma skin cancer using deep learning approach

Hussien,

Hassin Alasadi

2024

TELKOMNIKA

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In this study, the authors propose a deep learning (DL) approach for classifying melanoma skin cancer (MSC). They introduce a convolution neural network (CNN) model that consists of 27 layers, which are carefully designed to extract features from skin lesion images and classify them into melanoma and non-melanoma classes. The proposed CNN model comprises multiple convolution layers that apply filters to the input image to extract features such as edges, shapes, and patterns. Batch normalization layers that normalize the output of the convolution layers to accelerate the learning process and prevent overfitting follow these convolution layers. The performance of the proposed CNN model was evaluated on publicly available datasets of skin lesion images, and the findings showed that it outperformed several state-of-the-art methods for melanoma classification. The authors also conducted ablation studies to analyze each layer's contribution to the model's overall performance. The proposed DL approach has the potential to assist dermatologists in the early detection of MSC, which can lead to treatment that is more effective and improves patient outcomes. It also demonstrates the effectiveness of DL techniques for medical image analysis and highlights the importance of carefully designing and optimizing CNN models for high performance. The accuracy of the proposed system is 99.99%.

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Early Detection and Classification of Melanoma Skin Cancer

Cited by 10 publications

References 3 publications

Skin Lesion Detection Using Fuzzy Approach and Classification with CNN

Skin Lesion Detection Using Fuzzy Approach and Classification with CNN

Traffic Sign Detection and Recognition Model Using Support Vector Machine and Histogram of Oriented Gradient

Classification of melanoma skin cancer using deep learning approach

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