Analisa Perbandingan Algoritma CNN Dan MLP Dalam Mendeteksi Penyakit COVID-19 Pada Citra X-Ray Paru

Pada bulan Maret 2020 Organisasi Kesehatan Dunia atau WHO (World Health Organization) menyatakan bahwa COVID-19  sebagai pandemi global. Untuk mengendalikan penyebaran COVID-19 ini dibutuhkan diagnosis secara dini dan akurat. Saat ini, standar emas dalam diagnosis COVID-19 didasarkan pada Reverse Transcripttion Polymerase Chain Reaction (RT-PCR) yakni mengambil dari sample pasien secara langsung. Dalam menangani masalah yang ada dibutuhkan metode diagnostic alternative, seperti melakukan pengolahan dan analisis dari pencitraan medis. Tujuan dari penelitian ini adalah untuk melakukan diagnosis alternatif menggunakan data citra paru untuk dapat mengklasifikasi mana paru yang terkena COVID-19 dan mana paru yang sehat. Metode yang digunakan dalam mengklasifikasi data citra adalah dengan pendekatan Deep Learning. Pada kasus ini, penelitian ini akan melakukan perbandingan algoritma CNN dan MLP untuk dapat melihat mana dari keduanya yang menghasilkan hasil yang lebih baik. Hasil yang didapat menunjukkan bahwa CNN lebih unggul dengan akurasi sebesar 97,14% dibandingkan dengan MLP dengan akurasi sebesar 91,39%. Hal ini dikarena Algoritma CNN memiliki lebih banyak lapisan dibandingkan dengan MLP, serta Algoritma CNN dapat bekerja dengan baik pada data spasial

Skin Color Segmentation in RGB Color Space by Adaptive Network Based Fuzzy Inference System (ANFIS)

Skin color detection is a popular and useful technique because of the wide range of application in both human computer interactions and analyses based on diagnostic. Therefore, providing an appropriate method for pixel-like skin parts can solve many problems. The presented color segmentation algorithm works directly in RGB color space without having to convert the color space. Using Genfis3 function, we formed the Sugeno fuzzy network and clustered the data using fuzzy C-Mean (FCM) clustering rule and for each class and cluster we considered a Rule. In the next step, the output resulting from pseudo-polynomial data mapping is provided as the input to Adaptive Network Based Fuzzy Inference System (ANFIS)

On Assisted Living of Paralyzed Persons through Real-Time Eye Features Tracking and Classification using Support Vector Machines

Background: The eye features like eye-blink and eyeball movements can be used as a module in assisted living systems that allow a class of physically challenged people speaks – using their eyes. The objective of this work is to design a real-time customized keyboard to be used by a physically challenged person to speak to the outside world, for example, to enable a computer to read a story or a document, do gaming and exercise of nerves, etc., through eye features tracking Method: In a paralyzed person environment, the right-left, up-down eyeball movements act like a scroll and eye blink as a nod. The eye features are tracked using Support Vector Machines (SVMs). Results: A prototype keyboard is custom-designed to work with eye-blink detection and eyeball-movement tracking using Support Vector Machines (SVMs) and tested in a typical paralyzed person-environment under varied lighting conditions. Tests performed on male and female subjects of different ages showed results with a success rate of 92%. Conclusions: Since the system needs about 2 seconds to process one command, real-time use is not required. The efficiency can be improved through the use of a depth sensor camera, faster processor environment, or motion estimation