Early Detection of Skin Cancer Using Melanoma Segmentation technique

Sreelatha, Tammineni; Subramanyam, M. V.; Prasad, M. N. Giri

doi:10.1007/s10916-019-1334-1

Cited by 72 publications

(35 citation statements)

References 20 publications

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“…Researchers, practitioners and scholars are trying to contribute more and more towards in the field of medical imaging to get accurate results which also helps patients to recover soon. For problems like detection of cancer [30][31], detection of motor-imagery based electroencephalogram (EEG) signals [28] [29], multimodal optimization problems [26] [27] and Automatic rat brain segmentation from MRI [25] needs automated system which provides best and accurate results. Hence, we also proposed an automated system that detects skin cancer in its early stage using different image processing techniques.…”

Section: Figure 2 Benign Melanoma and Malignant Melanomamentioning

confidence: 99%

Automated Skin Lesion Detection towards Melanoma

Bibi

Hamid

Tehseen³

2018

ICST Transactions on Scalable Information Systems

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Skin cancer melanoma is one of the most dangerous cancers in the world. It is crucial to diagnose it in initial phases before it invades other organs. However, it requires an efficient and reliable diagnostic computer aided system for early detection. In this research study we aim to detect the skin cancer from two different image datasets. We also present the solution for images that contain disk objects. In initial phase we perform pre-processing, which is followed by segmentation phase. Then candidate dataset is evaluated using different measures such as accuracy, specificity, sensitivity and similarity. Obtained results are compared with results of techniques used in academic literature. We claim that our techniques give better accuracy for cancer detection.

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Section: Figure 2 Benign Melanoma and Malignant Melanomamentioning

confidence: 99%

Automated Skin Lesion Detection towards Melanoma

Bibi

Hamid

Tehseen³

2018

ICST Transactions on Scalable Information Systems

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“…Therefore, providing a method for the diagnosis of melanoma at an early stage is very useful and valuable [4]. In the last two decades, many studies have been performed on the rapid and accurate diagnosis of melanoma by dermoscopy images with diagnostic accuracy between 70% and 95% [5][6][7][8][9]. Recently, the use of machine vision and artificial intelligence as nondestructive tools in medical applications has been increasing.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of skin cancer based on soft computing techniques

et al. 2020

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Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.

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“…The more recent, various computerized methods are presented for detection and recognition of melanoma using dermoscopy data (Majumder & Ullah, ; Sreelatha, Subramanyam, & Prasad, ). Through dermoscopy, the skin images are captured, which gives more clarity of lesion spots.…”

Section: Introductionmentioning

confidence: 99%

“…The more recent, various computerized methods are presented for detection and recognition of melanoma using dermoscopy data (Majumder & Ullah, 2019;Sreelatha, Subramanyam, & Prasad, 2019).…”

mentioning

confidence: 99%

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

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The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

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Early Detection of Skin Cancer Using Melanoma Segmentation technique

Cited by 72 publications

References 20 publications

Automated Skin Lesion Detection towards Melanoma

Automated Skin Lesion Detection towards Melanoma

Computer-aided diagnosis of skin cancer based on soft computing techniques

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

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