Classification of Dermoscopy Images Using Naïve Bayesian and Decision Tree Techniques

Arasi, Munya A.; El-Horbaty, El-Sayed M.; El‐Dahshan, El‐Sayed A.

doi:10.1109/aicis.2018.00015

Cited by 20 publications

(19 citation statements)

References 15 publications

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“…[8], [14], [18], [26], our method DWT [12], [13], [15], [23], [24], [29], [30], [31] GWT [21], [27] CT [19], [20], [25] PCA…”

Section: Discussionmentioning

confidence: 99%

“…[8], [14], [19], [23], [29] Texture features [24], [25], [26], [28], [31] Color feature [19], [20], [21] Shape features [26], [27] Statistical parameters…”

Section: Discussionmentioning

confidence: 99%

“…From 2016 -2020: There are twelve published articles in this period. And there is a common dataset used in three articles [23,24,29] from DermQuest and two papers used PH2 dataset [27,31]. In the preprocessing stage, most of these papers applied different image sizes of the same implemented dataset.…”

Section: From 2000 -2005mentioning

confidence: 99%

“…Some papers applied the median filter to remove unwanted objects such as air bubbles as in references [20], [23,24]. Amir Reza suggested the hair removal from the images in [22], Spatarshi at [26], Uzma [27] and Akhiyar [31]. Each paper used different or similar classification methods to calculate the output results.…”

Section: From 2000 -2005mentioning

confidence: 99%

“…One type of WT was used in each research combined with the color feature as in [20,21]. The texture features are used in [24][25][26], [28,31]. The statistical parameters were implemented in [26].…”

Section: From 2000 -2005mentioning

confidence: 99%

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The Study of Recognition Methods Based on Wavelet Transform for Melanoma Detection

Almarei

Daqrouq

2020

JERR

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Skin cancer is one of the most cancers occurring in the world. Malignant melanoma is the most skin cancer type causing death around the world. Melanoma could be treated 100% if they are detected at earlier stages. In this paper, various melanoma detection systems were reviewed according to the year of publishing. All reviewed papers were based on feature extraction methods using wavelet transform (WT) in its two versions: Discrete wavelet transform (DWT), and wavelet packet transform (WPT) for melanoma recognition. Our methodology that was based on the WPT feature extraction and probabilistic neural network (PNN) was used for comparison. The ISIC database was used for differentiating between malignant (1110 images) and benign (1110 image) tumors. A (75% training /25% testing) verification system was applied. Many experiments were conducted using different parameters for each experiment. The support vector machine classifier (SVM) was the most common classifier combined with various types of wavelet features that have appeared in many kinds of literature during the last two decades, which achieved relatively the best accuracy ranged between [76% -98.29%]. In this paper, our combination method of the WPT and entropy was proposed and evaluated. Several experiments were conducted for testing. A Almarei and Daqrouq; JERR, 11(3): 46-61, 2020; Article no.JERR.55316 47 comparison manner was used for discussion of the investigation. The proposed method was an excellent detection method for melanoma regarding the complexity, where no preprocessing stage was conducted. Original Research Article

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“…[8], [14], [18], [26], our method DWT [12], [13], [15], [23], [24], [29], [30], [31] GWT [21], [27] CT [19], [20], [25] PCA…”

Section: Discussionmentioning

confidence: 99%

“…[8], [14], [19], [23], [29] Texture features [24], [25], [26], [28], [31] Color feature [19], [20], [21] Shape features [26], [27] Statistical parameters…”

Section: Discussionmentioning

confidence: 99%