Deteksi Kanker Kulit Melanoma dengan Linear Discriminant Analysis-Fuzzy k-Nearest Neigbhour Lp-Norm

Mentari, Mustika; Sari, Yuita Arum; Dewi, Ratih Kartika

doi:10.26594/register.v2i1.443

Cited by 3 publications

(7 citation statements)

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“…In this case, we applied RGBr as normalized color space from RGB. Some of color moments extraction is put in this phase as observed as well in the previous research [7,11]. All types of features extracted are explained in Table 1.…”

Section: Color Feature Extractionmentioning

confidence: 98%

“…There are 200 skin images consist of two categories. In this paper, we used secondary dataset based on previous research [11,17]. The pixel dimension of skin lesion image is 15 × 15 × 3 in RGB (Red, Green, Blue) color space.…”

Section: A Datasetmentioning

confidence: 99%

“…An enormous number of extracted features is likely to produce an overfitting condition, in which the number of samples in dataset is not balance between training and testing set [11]. In other sides, a huge number of features make biased results since the patterns of training data are not uniquely identified in the testing data.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Some studies have been conducted to examine the use of feature reduction methods. A research uses Linear Discriminant Analysis (LDA), one of feature selection methods, to anticipate the uneven distribution of features and the Fuzzy kNN method that is fixed by the LpNorm method [11]. The results of the study showed that LDA was able to increase the accuracy of melanoma detection by 63.6% compared to the one without LDA.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

Sari

Hapsani

Adinugroho

et al. 2021

Int. J. Artif. Intell. Res

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Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.

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Section: Color Feature Extractionmentioning

confidence: 98%

Section: A Datasetmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

Sari

Hapsani

Adinugroho

et al. 2021

Int. J. Artif. Intell. Res

Self Cite

View full text Add to dashboard Cite

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“…Suatu metode yang berbasis ciri-ciri holistic (menyeluruh) dari citra wajah and LDA (linear discriminant analysis) yang disederhanakan diusulkan sebagai algoritma alternative dari pengenalan wajah [7].Sistem Metode pengelan wajah fisherface merupakan pengembangan dari metode eigenface, metode fisherface menggunakan fisher's linear Discriminant Analysis (FLD/LDA) untuk mereduksi dimensi runag fitur [8] Metode LDA lebih optimal untuk melakukan diskriminasi atau klasifikasi dibandingkan dengan PCA yaitu dengan memaksimalkan rasio penyebaran antar kelas terhadap penyebaran dalam kelas untuk memudahkan klasifikasi sehingga fisherface dapat digunakan ada dataset wajah dengan variasi yang besar [9]. Tahapan dalam metode LDA atau Fisherface hampis sama dengan metode PCA hanya saja ditambahkan proses perhitungan untuk mencari sebaran.…”

Section: Lda (Linear Discriminant Analysis)unclassified

Perbandingan Metode Pengenalan Wajah Melalui Surveillance Berbasis Pengenalan Wajah

Putra¹,

Kartini²

2021

SINTECH Journal

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Until now, the facial recognition method is still very difficult to do, especially when the facial confirmation process is accurate in real time. Facial recognition methods that have been tested, such as eigenface, Local Binary Pattern Histogram (LBPH), and fisherface, are feasible methods to be tested directly by applying these three methods to facial recognition-based surveillance systems. This study aims to compare the level of real-time accuracy in personal identification on the three methods through 4 parameters, namely accuracy, FAR (False Acceptance Rate), FRR (False Rejection Rate), and time condition, namely lighting conditions based on time, namely morning, noon, afternoon and evening. Based on the results of the tests that have been done, the average accuracy is obtained, namely the highest average with the eigenface method with an accuracy of 73.64%, FAR 0.11%, FRR 0.15%, the LBPH method obtains the highest average with an accuracy of 80, 91%, FAR 0.13%, FRR 0.07%, and finally fisherface got the highest average with 90.00% accuracy, 0.05% FAR, 0.05% FRR, in identifying personal. The results obtained by the Fisherface method tend to have the highest accuracy value based on the average both in terms of accuracy and the lighting conditions that have been tested.

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Aplikasi Pengenalan Pola Penyakit Kulit Menggunakan Algoritma Linear Discriminant Analysis

Ghani

Intan

Salman

2022

CogITo Smart Journal

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Sometimes, someone underestimates to check up skin disease unless the disease has affected his face or body in severe condition. The checkup fees for skin diseases are relatively expensive because they require a specialist. While the reach of society, in general, is the lower community. The purpose of this study is to bridge the gap between the patient and the examination of the disease based on the patient's skin image. The methods used in feature extraction and classification are Linear Discriminant Analysis LDA and Euclidean Distance respectively. LDA performs image feature extraction through a matrix operation process and distinguishing features in the same class and different classes. Classification will give the output of disease: abscess, eczema, ringworm, and urticaria. The accuracy results obtained are 80%. The next research is on adding features in the form of skin color so that it can be an input feature in the image as well as to improve its performance in the future. This application can be an alternative initial checkup for patients. It will detect the type of skin disease be suffered before consulting an expert.

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Deteksi Kanker Kulit Melanoma dengan Linear Discriminant Analysis-Fuzzy k-Nearest Neigbhour Lp-Norm

Cited by 3 publications

References 0 publications

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

Perbandingan Metode Pengenalan Wajah Melalui Surveillance Berbasis Pengenalan Wajah

Aplikasi Pengenalan Pola Penyakit Kulit Menggunakan Algoritma Linear Discriminant Analysis

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