Skin Cancer Detection Using Machine Learning Algorithms

Ramachandro, M.; Daniya, T.; Saritha, B.

doi:10.1109/i-pact52855.2021.9696874

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…It describes the probability of two pairs of gray levels occurring together in an image, allowing for the extraction of texture features to differentiate between various textures. This study uses GLCM to extract four important different features [19], contrast, correlation, energy, and homogeneity.  Diameter, it is a significant feature for detecting skin cancer.…”

Section: Feature Extractionmentioning

confidence: 99%

Stand-Alone Melanoma Diagnostic System Using the Raspberry PI 4

Khaleel,

Basheer

2023

EJCSIT

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Skin cancer, in general, is a growing threat to human health. Melanoma skin cancer is one of the most severe types of skin cancer in humans. In the initial stages, melanoma affects the skin in general, and it may develop and spread to other parts of the human body in its advanced stages. Early detection can reduce its severity and improve treatment outcomes. In this study, the proposed system includes image processing techniques and feature extraction by combining Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM), in addition to enhancing the extracted features with the diameter feature. A Support Vector Machine (SVM) model with a radial basis function (RBF) kernel was employed to train and predict the results of the test data on the Raspberry Pi 4 platform. The available International Skin Imaging Collaboration (ISIC) dataset of skin images was used in the training and testing of this system. The test results showed that the proposed system achieves high specificity (99.35%), medium sensitivity (75.35%), and comprehensive accuracy (93.71%).

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Section: Feature Extractionmentioning

confidence: 99%

Stand-Alone Melanoma Diagnostic System Using the Raspberry PI 4

Khaleel,

Basheer

2023

EJCSIT

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“…They appear to use the residual learning approach that deals with extreme learning problems. Ramachandro et al used the same data balancing algorithm and data set that we used in the article in their study [23]. However, in the study, it is seen that only images containing akiec, bcc, df and mel types are classified, not seven different types of skin cancer.…”

Section: Introductionmentioning

confidence: 99%

CNN Tabanlı Derin Öğrenme Yaklaşımı ile Kanser Görüntülerinin Sınıflandırılması

ÇETİNER

2022

NÖHÜ Müh. Bilim. Derg.

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İnsanların güneşin zararlı ışınlarının sebebiyet verdiği etkiler sonucunda halk arasında melanoma (mel), dermafibroma (df), ve vascular (vasc), bening keratosis (bkl), melanocytic nevi (nv), basal cell carcinoma (bcc), actinic keratosis (akiec) olarak bilinen türler sıklıkla görülmektedir. Biyomedikal cihazlar kullanılarak elde edilen cilt görüntüleri üzerinde yapılan derin öğrenme analizleri ile girdi olarak verilen cilt görüntüsünün kanser ise hangi cilt kanseri olduğu belirlenebilmektedir. Bu maksatla cilt alanında çalışan sağlık uzmanlarının iş yoğunluğunu düşürecek, hızlı tanı ve sınıflandırma yapabilecek CNN tabanlı bir derin öğrenme modeli önerilmiştir. Swish ve ReLU aktivasyon fonksiyonunun avantajlarından faydalanılarak geliştirilen CNN modeli akiec, bcc, bkl, df, nv, vasc, mel cilt kanseri sınıf türlerinde 0.99%, 0.99%, 0.96%, 0.99%, 0.92%, 0.99%, 0.95% F1 score değerlerini elde etmiştir

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Skin lesion classification on dermatoscopic images using effective data augmentation and pre-trained deep learning approach

Bozkurt

2022

Multimed Tools Appl

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Skin Cancer Detection Using Machine Learning Algorithms

Cited by 7 publications

References 16 publications

Stand-Alone Melanoma Diagnostic System Using the Raspberry PI 4

Stand-Alone Melanoma Diagnostic System Using the Raspberry PI 4

CNN Tabanlı Derin Öğrenme Yaklaşımı ile Kanser Görüntülerinin Sınıflandırılması

Skin lesion classification on dermatoscopic images using effective data augmentation and pre-trained deep learning approach

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