Melanoma Detection and Classification in Digital Dermoscopic Images Using Machine Learning

2021 ◽  
pp. 493-504
Author(s):  
K. Senthil Kumar ◽  
S. Varalakshmi ◽  
G. Sathish Kumar ◽  
T. Kosalai
Keyword(s):  
Machine Learning ◽  
Melanoma Detection

Diagnosing of Dermoscopic Images using Machine Learning approaches for Melanoma Detection

2020 ◽  
Author(s):  
Faiza ◽  
Syed Irfan ullah ◽  
Abdus Salam ◽  
Farhat Ullah ◽  
Muhammad Imad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Melanoma Detection

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

scholarly journals A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

2021 ◽  
Vol 14 (3) ◽  
pp. 1231-1247
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions ◽  
Learning Framework ◽  
Melanoma Detection ◽  
Pigmented Skin Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

scholarly journals Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues

2020 ◽  
Vol 25 (11) ◽  
Author(s):  
Daniel S. Gareau ◽  
James Browning ◽  
Joel Correa Da Rosa ◽  
Mayte Suarez-Farinas ◽  
Samantha Lish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Imaging Biomarker ◽  
Melanoma Detection ◽  
Interpretable Machine Learning

scholarly journals Diagnosis of Skin Cancer Based on Image Processing Using Machine Learning

2021 ◽  
pp. 350-357
Author(s):  
Snehal Vijay Kamble ◽  
Dr. P. R. Gumble
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Skin Cancer ◽  
Malignant Melanoma ◽  
Entire Body ◽  
Melanoma Detection ◽  
Melanoma Diagnosis ◽  
Processing Techniques ◽  
Analysis Of Performance ◽  
Aided Diagnosis

In a human body, skin is the core part, which helps to cover the muscles, bones what's more with the entire body. These days numerous people are suffering from skin cancer. Malignant melanoma is the deadliest form of skin cancer. The most serious type of cancer is Melanoma, which is the enormous type of skin malignant growth and the extent of these skin cancer is increasing day by day. Melanoma can be easily treatable if detected in early stages. Clinical as well as automated methods are being used for melanoma diagnosis. Image-based computer aided diagnosis systems have great potential for early malignant melanoma detection. Recognizing the type of skin cancer automatically from the images can assist in the quick diagnosis and enhanced accuracy saving valuable time. This paper presented the automated diagnosis of skin cancer from input digital image by analyzing image using Image Processing techniques with applying intelligence using Machine Learning. The simulation and analysis of performance of proposed system is carried out using MATLAB Software.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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