scholarly journals Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1976
Author(s):  
Jingyu Kim ◽  
Su Young Jeong ◽  
Byung-Chul Kim ◽  
Byung-Hyun Byun ◽  
Ilhan Lim ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Fdg Pet ◽  
Texture Features ◽  
Test Accuracy ◽  
Tumor Center ◽  
18F Fdg

We compared the accuracy of prediction of the response to neoadjuvant chemotherapy (NAC) in osteosarcoma patients between machine learning approaches of whole tumor utilizing fluorine−18fluorodeoxyglucose (18F-FDG) uptake heterogeneity features and a convolutional neural network of the intratumor image region. In 105 patients with osteosarcoma, 18F-FDG positron emission tomography/computed tomography (PET/CT) images were acquired before (baseline PET0) and after NAC (PET1). Patients were divided into responders and non-responders about neoadjuvant chemotherapy. Quantitative 18F-FDG heterogeneity features were calculated using LIFEX version 4.0. Receiver operating characteristic (ROC) curve analysis of 18F-FDG uptake heterogeneity features was used to predict the response to NAC. Machine learning algorithms and 2-dimensional convolutional neural network (2D CNN) deep learning networks were estimated for predicting NAC response with the baseline PET0 images of the 105 patients. ML was performed using the entire tumor image. The accuracy of the 2D CNN prediction model was evaluated using total tumor slices, the center 20 slices, the center 10 slices, and center slice. A total number of 80 patients was used for k-fold validation by five groups with 16 patients. The CNN network test accuracy estimation was performed using 25 patients. The areas under the ROC curves (AUCs) for baseline PET maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and gray level size zone matrix (GLSZM) were 0.532, 0.507, 0.510, and 0.626, respectively. The texture features test accuracy of machine learning by random forest and support vector machine were 0.55 and 0. 54, respectively. The k-fold validation accuracy and validation accuracy were 0.968 ± 0.01 and 0.610 ± 0.04, respectively. The test accuracy of total tumor slices, the center 20 slices, center 10 slices, and center slices were 0.625, 0.616, 0.628, and 0.760, respectively. The prediction model for NAC response with baseline PET0 texture features machine learning estimated a poor outcome, but the 2D CNN network using 18F-FDG baseline PET0 images could predict the treatment response before prior chemotherapy in osteosarcoma. Additionally, using the 2D CNN prediction model using a tumor center slice of 18F-FDG PET images before NAC can help decide whether to perform NAC to treat osteosarcoma patients.

Can pretreatment 18F-FDG PET tumor texture features predict the outcomes of osteosarcoma treated by neoadjuvant chemotherapy?

2019 ◽  
Vol 29 (7) ◽  
pp. 3945-3954 ◽  
Author(s):  
Hongjun Song ◽  
Yining Jiao ◽  
Weijun Wei ◽  
Xuhua Ren ◽  
Chentian Shen ◽  
...  
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Fdg Pet ◽  
Texture Features ◽  
18F Fdg

scholarly journals PERANCANGAN APLIKASI MOBILE UNTUK KLASIFIKASI SAYURAN MENGGUNAKAN DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 300-306
Author(s):  
Muhamad Jaelani Akbar ◽  
Mochamad Wisuda Sardjono ◽  
Margi Cahyanti ◽  
Ericks Rachmat Swedia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Test Accuracy ◽  
Final Test ◽  
Testing Data

Sayuran merupakan sebutan bagi bahan pangan asal tumbuhan yang biasanya mengandung kadar air tinggi dan dikonsumsi dalam keadaan segar atau setelah diolah secara minimal. Keanekaragaman sayur yang terdapat di dunia menyebabkan keragaman pula dalam pengklasifikasian sayur. Oleh karena itu diperlukan adanya pendekatan digital agar dapat mengenali jenis sayuran dengan cepat dan mudah. Dalam penelitian ini jumlah jenis sayuran yang digunakan sebanyak 7 jenis diantara: brokoli, jagung, kacang panjang, pare, terung ungu, tomat dan kubis. Dataset yang digunakan berjumlah 941 gambar sayur dari 7 jenis sayur, ditambah 131 gambar sayur dari jenis yang tidak terdapat pada dataset, selain itu digunakan 291 gambar selain sayuran. Untuk melakukan klasifikasi jenis sayuran digunakan algoritme Convolutional Neural Network (CNN), yang merupakan salah satu bidang ilmu baru dalam Machine Learning dan berkembang dengan pesat. CNN merupakan salah satu algoritme yang terdapat pada metode Deep Learning dengan memiliki kemampuan yang baik dalam Computer Vision, salah satunya yaitu image classification atau klasifikasi objek citra. Uji coba dilakukan pada lima perangkat selular berbasiskan sistem operasi Android. Python digunakan sebagai bahasa pemrograman dalam merancang aplikasi mobile ini dengan menggunakan modul Tensor flow untuk melakukan training dan testing data. Metode yang dapat digunakan dalam melakukan klasifikasi citra ini yaitu Convolutional Neural Network (CNN). Hasil final test accuracy yang diperoleh yaitu didapat keakuratan mengenali jenis sayuran sebesar 98.1% dengan salah satu hasil pengujian yaitu klasifikasi sayur jagung dengan akurasi sebesar 99.98049%.

scholarly journals Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDF PET/CT, EZRIN and KI67

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2671
Author(s):  
Byung-Chul Kim ◽  
Jingyu Kim ◽  
Kangsan Kim ◽  
Byung Hyun Byun ◽  
Ilhan Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Texture Features ◽  
Image Texture ◽  
Chemotherapy Response ◽  
Test Accuracy ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
Pediatric Osteosarcoma

Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. 18F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data.

scholarly journals IMPLEMENTASI DEEP LEARNING FLOWER SCANNER MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Hanissa Anggraini Pratiwi ◽  
Margi Cahyanti ◽  
Missa Lamsani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Test Accuracy ◽  
Model Data ◽  
Final Test

Bunga atau kembang adalah alat reproduksi seksual pada tumbuhan berbunga. Pada bunga terdapat organ reproduksi, yaitu benang sari dan putik. Pada beberapa spesies, bunga majemuk dapat dianggap awam sebagai bunga (tunggal), ada sekitar 391.000 spesies tanaman vaskular yang saat ini diketahui sains, dimana sekitar 369.000 spesies (atau 94 persen) adalah tanaman berbunga. Klasifikasi jenis bunga merupakan pekerjaan yang membutuhkan waktu dan pengetahuan. Perkembangan visi komputer memungkinkan otomatisasi klasifikasi jenis bunga dengan efisien dan akurat. Deep Learning merupakan cabang ilmu machine learning berbasis Jaringan Saraf Tiruan (JST) atau bisa dikatakan sebagai perkembangan dari JST. Dalam Deep Learning, sebuah komputer belajar mengklasifikasi secara langsung dari gambar atau suara. Dengan menggunakan teknologi Deep Learning yang merupakan salah satu bidang ilmu baru dalam Machine learning dan berkembang dengan sangat pesat. Deep Learning memiliki kemampuan yang baik dalam Computer Vision, yaitu Image Classification atau kalsifikasi objek pada citra dalam bentuk dua dimensi misalnya gambar dan suara. Hasil final test accuracy yang diperoleh yaitu didapat keakuratan sebesar 100% dengan salah satu hasil pengujian yaitu klasifikasi bunga mawar  dengan akurasi sebesar 99,30%. Model data latih menggunakan dengan total dataset 460 gambar (yang diambil melalui pencarian gambar pada Google Image) sebanyak 30 kali dilatih, di mana setiap 13 langkah terhitung 1 training. Sehingga menghasilkan keluaran nilai akurasi dari data yang telah dilatih (val_acc) dan nilai akurasi dari data yang hilang atau miss (val_loss). Diharapkan dengan adanya implementasi aplikasi ini dapat membantu pengguna untuk memelihara bunga hias dengan jenis sesuai dengan keinginan.

scholarly journals Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

2021 ◽  
Vol 12 (2) ◽  
pp. 123
Author(s):  
A A JE Veggy Priyangka ◽  
I Made Surya Kumara
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Rice Plant ◽  
Data Augmentation ◽  
Rice Production ◽  
Plant Diseases ◽  
Test Accuracy ◽  
Neural Network Method ◽  
Network Method

Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.

scholarly journals Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0223141 ◽  
Author(s):  
Paul Blanc-Durand ◽  
Maya Khalife ◽  
Brian Sgard ◽  
Sandeep Kaushik ◽  
Marine Soret ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attenuation Correction ◽  
Fdg Pet ◽  
Deep Convolutional Neural Network ◽  
18F Fdg

scholarly journals Texture Recognition Based on Perception Data from a Bionic Tactile Sensor

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5224
Author(s):  
Shiyao Huang ◽  
Hao Wu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithms ◽  
Tactile Sensor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Accuracy ◽  
Texture Recognition ◽  
Vibration Data

Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness.

scholarly journals PENYAKIT TANAMAN APEL DARI CITRA DAUN DENGAN CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
Author(s):  
M Ridwan Dwi Septian ◽  
Andi Asrafil Ardan Paliwang ◽  
Margi Cahyanti ◽  
Ericks Rachmat Swedia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Test Accuracy ◽  
Final Test

Tanaman Apel merupakan buah tahunan yang berasal dari daerah Asia Barat dengan iklim sub tropis. Di Indonesia tanaman Apel ditanam sejak tahun 1934 hingga saat ini. Tanaman Apel dapat tumbuh dan berbuah baik di daerah dataran tinggi. Para petani biasanya melakukan pencegahan penyakit atau hama dengan melakukan penyemprotan setiap 1 – 2 minggu sekali dengan dosis ringan. Pencegahan ini agar penyakit/hama dapat segera ditanggulangi dan baik jika dilakukan pada pagi atau sore hari. Terkadang petani juga membutuhkan seorang pakar dalam menentukan jenis hama/penyakit pada tanaman Apel agar dapet memberikan solusi terbaik. Oleh karena itu diperlukan adanya pendekatan digital agar dapat mengenali beragam jenis hama/penyakit tanaman Apel dengan cepat dan mudah. Teknologi Deep Learning, merupakan salah satu bidang ilmu baru dalam Machine Learning dan berkembang dengan depat. Deep Learning memiliki kemampuan yang baik dalam Computer Vision, salah satunya yaitu image classification atau klasifikasi objek pada citra. Metode yang dapat digunakan dalam melakukan klasifikasi citra ini yaitu Convolutional Neural Network (CNN). Berdasarkan hasil uji coba, aplikasi berhasil diimplementasikan dengan baik menggunakan framework  dart berbasis android dengan hasil final test accuracy yang diperoleh yaitu didapat keakuratan sebesar 97,1%.

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