A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images

Various types of algorithms have been widely used for image segmentation in digital image processing. Every algorithm has features that make it unique to be applied to specific cases. One of the applications of image segmentation is to detect white blood cells. Certain objects such as blood cells must be able to be well segmented because their existence is very crucial to support the accuracy of disease detection related to haematology or the branch of medical science that studies the morphology of blood and blood-forming tissues. Three image segmentation algorithms were compared through this study: Seed Region Growing, Otsu Thresholding and Active Contour Without Edge. Comparative analysis of the three algorithms was done by counting the number of white blood cell objects that were successfully segmented with the actual number of cells that were counted manually. A total of 30 images of blood smears were taken from people suffering from acute myeloid leukemia M1. The average accuracy values from each algorithm were used to determine which image segmentation algorithm is the most suitable for application in the case of white blood cells segmentation. The results showed that Active Contour Without Edge is the most appropriate among the other algorithm

Peningkatan HSV dan Haar-Like Feature pada Aplikasi Identifikasi Kematangan Buah Tomat Berbasis Android

Tomat adalah buah yang terkenal karena memiliki banyak nutrisi penting dan bermanfaat seperti antioksidan, vitamin C dan A untuk makanan sehari-hari manusia. Memetik tomat dengan tangan merupakan pekerjaan yang berat dan memakan waktu. Karena itu, untuk mengatasi masalah ini, tomat perlu diambil secara otomatis dengan bantuan teknologi. Baru-baru ini otomatisasi panen buah memperoleh popularitas besar. Untuk memandu robot pemanen mengambil buah dengan benar, penting untuk mendeteksi dan menemukan lokasi buah matang merah dengan benar. Maka dibutuhkan aplikasi untuk identifikasi kematangan buah tomat. Dalam penelitian ini, algoritma pendeteksian tomat matang berdasarkan ruang warna HSV (Hue, Saturation, Value) yang ditingkatkan dengan haar-like feature.  Metode ini diterapakan pada aplikasi berbasis android. Pada tahap pertama, transformasi HSV digunakan untuk menghilangkan latar belakang dan hanya mendeteksi tomat merah. Kemudian operasi morfologis diterapkan untuk memodifikasi buah yang terdeteksi. Hasil penelitian mampu mendeteksi tomat matang merah dengan peningkatan HSV dan haar-like feature

Rekayasa Perangkat Lunak Aplikasi Presensi Mobile Menggunakan Metode Deep Learning

Facial recognition research has its challenges due to faces complexity, ranging from facial expressions and certain conditions that make facial recognition an exciting research experiment. Moreover, many-oriented applications of machine learning have moved to devices edge, and-based facial recognition is no exception mobile. Seeing the ongoing development of facial pattern recognition algorithms such as Viola Jones, Backpropagation, this research uses the MobileFaceNet  mobile CNN model which is currently popular to be implemented in the mobile-based facial recognition presence application at the Information and Computer Engineering Education (PTIK) FKIP UNS. The deep learning method is a method for understanding and classifying objects. In the developed application, a face is captured in an image. This research uses the help of the flutter framework and the Tensorflow Lite library to develop a presence application mobile facial recognition in real-time. This paper aims to determine the value of the memorization and generalization algorithms model of CNN MobileFaceNet  on the application.  A trial of the system has been carried out by involving 30 volunteers in the testing from 2016-2019 PTIK students by random sampling. Each test was carried out for 10 iterations. From the test results, the system memorization value is 84.5%. On the other hand, the generalization results get 70% in recognizing identical but not similar images correctly. In terms of memorization and generalization, these results are better than similar studies using backpropagatio

Workshop And Motivation For Improving Student Skills Through The Information And Communications Technology

In the digital age, the role of information technology is needed to face competition in the community. Information and communication technology is an important element in contributing to changes that are fundamental to the structure of operations and management of organizations, education, transportation, health, and research. The internet is like two sides of a coin, the content offered is positive and negative, both are very dependent on the behavior of its users. The ease of access to the internet is increasingly being felt by the public with increasingly cheap hardware such as tablets and laptops as well as wider connection support. Various efforts to stem negative information continue to be pursued by various elements of society, but it is not effective if the user behavior is not changed. Teenagers are among the most vulnerable in the misuse of advances in internet technology, so it needs serious efforts to provide the right knowledge and skills in utilizing these advancements. By conducting workshops and motivation to improve the abilities and skills of Girimarto 1 High School students, it is hoped that school students can face the development of the digital era more readily. The results of this training gained a high level of satisfaction with the material that had been carried out

Identifikasi Penyakit Daun pada Tanaman Padi Menggunakan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) dan Metode K-Nearest Neighbour (KNN)

Negara Indonesia merupakan salah satu negara agrikultur, di mana bidang pertanian berperan penting dalam menjaga keberlangsungan hidup. Hal ini dikarenakan, sebagian besar masyarakat Indonesia menggunakan beras sebagai bahan pangan pokok mereka. Sedangkan ketersedian bahan pangan pokok masyarakat sudah berkurang, karena adanya alih fungsi lahan-lahan pertanian menjadi perumahan, industry, dan lain-lain. Bukan hanya itu saja, permasalahan lain yang dapat menurunkan ketersediaan bahan pangan yaitu seperti, kondisi iklim atau cuaca, system pengairan, serangan hama dan masih banyak lagi permasalahan yang dapat mengakibatkan panen menjadi kurang maksimal. Penggunaan teknologi dalam bidang pertanian seharusnya menjadi lebih mudah dan membantu para petani dalam mendeteksi penyakit yang menyerang daun padi. Karena itu, deteksi dan klasifikasi hama pada daun padi perlu dilakukan untuk mengevaluasi akurasi, presisi, dan recall menggunakan perhitungan matriks kebingungan (confusion matrix) dengan menerapkan algoritma K-Nearest Neighbors (KNN). Dari hasil klasifikasi tersebut menghasilkan nilai akhir akurasi paling tinggi yaitu sebesar 73% pada jarak piksel (d) yaitu 5 dan nilai tetangga (k) yaitu 3 pada offset 0°. Hal ini menunjukkan bahwa algoritma KNN cukup baik dalam melakukan klasifikasi

Classification of Acute Myeloid Leukemia Subtypes M1, M2 and M3 Using K-Nearest Neighbor

Leukemia is a malignant disease caused by the massive and rapid development of white blood cells in the bone marrow. These excessive white blood cells begin to interfere with the body’s mechanism rather than fighting infection. Acute Myeloid Leukemia (AML) is one of the four main types of leukemia with eight subtypes, M0 to M7. AML M1, M2, and M3 have similarities, making them more difficult to distinguish from the other types. Furthermore, they are usually identified by calculating the ratio of myeloblast, promyelocyte, and monoblastic. This research aims to apply the k-Nearest Neighbor (k-NN) in classifying these cell types. k-NN is an algorithm used for classification based on a similarity measure. In cases of finding the best number of neighborhoods, trial and error were conducted. The features needed for classification are cell area, perimeter, roundness, nucleus ratio, mean and standard deviation. Four distance metrics such as Euclidean, Manhattan, Minkowski, and Chebyshev were used in this research. The results show that the Euclidean, Manhattan, Chebyshev, and Minkowski distance successfully identified 207 out of 300 objects at K=18, 197 out of 300 objects at K=13,  209 out of 300 correct objects at K=9, and 210 out of 300 objects at K=7.  In conclusion, Minkowski was chosen as the best distance metric for KNN in classifying leukemia-forming blood cells. Furthermore, the accuracy, recall, and precision values of KNN with Minkowski distance obtained from 5-fold cross-validation were 80.552%, 44.145%, and 42.592%, respectively

Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy

Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy