A Comparison of Human Crafted Features and Machine Crafted Features on White Blood Cells Classification

Kurniadi, Felix Indra; Putri, Vinnia Kemala

doi:10.1088/1742-6596/1201/1/012045

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…F.I. Kurniadi et al [38] used the VGG-16 model in combination with local binary pattern for extracting features. Makem et al [39] make use of color space transformation using two-color spaces, cyan-magenta-yellow-key (CMYK) and hue-saturation-value (HSV), along with Otsu's thresholding to segment the blood cell nuclei for feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…F.I. Kurniadi et al[38] 2019 Deep CNN (machine-crafted) and Local Binary Pattern (LBP) (hand-crafted) features for classification Private 94.68% Makem et al [39] 2020 Color space transformation utilizing the CMYK and HSV color spaces, followed by Otsu's thresholding for segmentation-PCA based feature fusion This work contributes a new CNN-based architecture called 4B-AdditionNet (see Fig. 4 for block architecture and Table 2 for structural detail).…”

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confidence: 99%

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Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

Raza

Shah

Sharif³

et al. 2021

Complex Intell. Syst.

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White blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoimmune disorders, cancer, sarcoidosis, aplastic anemia, leukemia, tuberculosis, etc. A lot of these diseases and disorders can be extremely painful and often result in death. Leukemia is among the more common types of blood cancer and when left undetected leads to death. An early diagnosis is necessary which is possible by looking at the shapes and determining the numbers of young and immature WBCs to see if they are normal or not. Performing this task manually is a cumbersome, expensive, and time-consuming process for hematologists, and therefore computer-aided systems have been developed to help with this problem. This paper proposes an improved method of classification of WBCs utilizing a combination of preprocessing, convolutional neural networks (CNNs), feature selection algorithms, and classifiers. In preprocessing, contrast-limited adaptive histogram equalization (CLAHE) is applied to the input images. A CNN is designed and trained to be used for feature extraction along with ResNet50 and EfficientNetB0 networks. Ant colony optimization is used to select the best features which are then serially fused and passed onto classifiers such as support vector machine (SVM) and quadratic discriminant analysis (QDA) for classification. The classification accuracy achieved on the Blood Cell Images dataset is 98.44%, which shows the robustness of the proposed work.

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

Raza

Shah

Sharif³

et al. 2021

Complex Intell. Syst.

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“…Kemudian menurunkan sebanyak 50% neuron secara random pada dropout layer untuk membuat network agar tidak fit dengan training data. Selanjutnya dimasukkan ke dalam softmax untuk proses klasifikasi [10].…”

Section: A Transfer Learningunclassified

Klasifikasi Topeng Cirebon menggunakan Metode Convolutional Neural Network

Kurniadi¹

2021

JATISI

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Cirebon mask is one of the intangible cultural heritage in Indonesia. It is one of the prominent cultural assets from Cirebon and becoming one of the identity Cirebon culture. However, the current condition people tend to forget the cultural asset and lack of help from the government makes the Cirebon mask become the third-rate assets. Our concern lays on the extinction of this Mask. We want to implement digitation and automatic identification using image processing techniques. In this paper, we applied the Convolutional Neural Network for Cirebon Mask classification.

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“…K-Nearest Neighbour (KNN) merupakan salah satu algoritma sederhana yang sering digunakan dikarenakan banyak data yang secara linear dapat dipisahkan. Hal inilah yang membuat KNN merupakan salah satu algoritma yang tangguh [9]. Secara garis besar KNN melihat kedekatan antar kdata.…”

Section: K-nearest Neighbourunclassified

“…Secara garis besar KNN melihat kedekatan antar kdata. Dimana data test yang dimasukan akan melihat persebaran tetangga terdekat dan mengambil kelas berdasarkan kelas terbanyak dari tetangga terdekat [9].…”

Section: K-nearest Neighbourunclassified

Gray Level Co-ocurence Matrix untuk Pengekstrasian Ciri Topeng Cirebon

Kurniadi¹,

Putri

2020

Ultimatics

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Topeng Cirebon merupakan salah satu kekayaan budaya dalam Indonesia. Salah satunya adalah Topeng Cirebon yang merupakan salah satu identitas yang tidak terpisahkan dalam budaya kota Cirebon. Sayangnya, orang-orang tidak terlalu tertarik dengan kebudayaan daerah dan kurangnya bantuan pemerintahan setempat membuat topeng Cirebon semakin ditinggalkan. Hal ini ditakutkan dikemudian hari topeng Cirebon akan dilupakan. Tujuan utama dari penelitian ini adalah melakukan digitalisasi topeng Cirebon dan identifikasi secara automatis menggunakan pendekatan pengolahan citra. Penelitian ini menggunakan Gray Level Co-occurence Matrix sebagai fitur utama dan K-Nearest Neighbour sebagai klasifier. Hasil akurasi terbaik dari penelitian ini adalah sebesar 40.67%

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A Comparison of Human Crafted Features and Machine Crafted Features on White Blood Cells Classification

Cited by 6 publications

References 10 publications

Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

Klasifikasi Topeng Cirebon menggunakan Metode Convolutional Neural Network

Gray Level Co-ocurence Matrix untuk Pengekstrasian Ciri Topeng Cirebon

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