In this research, a backpropagation neural network and Latent Semantic Analysis were used to assess the quality of Thai-language essays written by high school students in the subject matter of historical royal Thai literatures. Forty essays written in response to a question were each evaluated by high school teachers and assigned a human score. In the first experiment, we used raw term frequency vectors of the essays and their corresponding human scores to train the neural network and obtain the machine scores. In the second experiment, we pre-processed the raw term frequency vectors using Latent Semantic Analysis technique prior to feeding them to the neural network. The experimental results show that the addition of Latent Semantic Analysis technique improves scoring performance.

Osteosarcoma is a rare bone cancer which is more common in children than in adults and has a high chance of metastasizing to the patient’s lungs. Due to initiated cases, it is difficult to diagnose and hard to detect the nodule in a lung at the early state. Convolutional Neural Networks (CNNs) are effectively applied for early state detection by considering CT-scanned images. Transferring patients from small hospitals to the cancer specialized hospital, Lerdsin Hospital, poses difficulties in information sharing because of the privacy and safety regulations. CD-ROM media was allowed for transferring patients’ data to Lerdsin Hospital. Digital Imaging and Communications in Medicine (DICOM) files cannot be stored on a CD-ROM. DICOM must be converted into other common image formats, such as BMP, JPG and PNG formats. Quality of images can affect the accuracy of the CNN models. In this research, the effect of different image formats is studied and experimented. Three popular medical CNN models, VGG-16, ResNet-50 and MobileNet-V2, are considered and used for osteosarcoma detection. The positive and negative class images are corrected from Lerdsin Hospital, and 80% of all images are used as a training dataset, while the rest are used to validate the trained models. Limited training images are simulated by reducing images in the training dataset. Each model is trained and validated by three different image formats, resulting in 54 testing cases. F1-Score and accuracy are calculated and compared for the models’ performance. VGG-16 is the most robust of all the formats. PNG format is the most preferred image format, followed by BMP and JPG formats, respectively.

Osteosarcoma nodule that metastasized to the patient's lungs was difficult to detect due to limited cases caused by its rarity. The traditional method for finding lung nodules is manually done by radiologists by looking at CT-scanned images. As a result, the error rate for reading lung metastasized nodules ranged from 29 to 42 percent, while the permissible mistake rate for reading should be less than 29 percent. Advanced computer-aid techniques such as image processing and machine learning can help doctors to identify the Osteosarcoma lung nodules easier and more accurately. Convolutional Neural Networks (CNNs) are promising techniques since they could be trained by experienced radiologists. Nodule location and size information was critical for treatments that were obtained by object detector CNNs models. In this research, the Single Shot Detection (SSD) framework combined with the VGG16 backbone, SSD-VGG16, was implemented to obtain bounding box locations and sizes when each box represents one Osteosarcoma nodule with the confidence score. The SSD-VGG16 was selected due to its superior performance. The patient's CTscanned images dataset collected from 202 patient cases was provided by Lerdsin hospital and used for training and validating the SSD-VGG16 model. The trained SSD-VGG16 model was trained based on two loss functions which are class confidence and location losses. Then, the trained model experimented with unseen CT-scanned images. The performance scores were calculated. The Result was analyzed and concluded. Finally, SSD-VGG16 shows the ability to detect and locate the nodules efficiently and has less error compared to the traditional method.INDEX TERMS CNNs, Lung nodule, Metastatic cancer, Osteosarcoma, SSD-VGG16 I. INTRODUCTIONOsteosarcoma [1-5] is one of the most common types of bone cancer about 28% of all cancer [6][7][8]. This bone cancer can metastasize to other organs, especially the lungs. The majority of osteosarcoma patients suffered clinically detectable metastases from bone to the lung [9][10][11]. Regularly, the lung nodule detection standard method is to read the computed tomography (CT) scan file. In Thailand, the recorded cases of this disease were very rare therefore CTscanned images are limited. As the result, doctors are not familiar with this disease and find it difficult to diagnose. According to the previous studies, the rate of reading nodules in the lung metastasis group had an error rate between 29 to 42 percent [12][13][14][15][16][17][18][19][20][21][22]. This high error rate may result in missed diagnostics, particularly in false-negative cases. A false negative prediction leads to delayed treatment causing difficulty in curing the patient.Computer-aided diagnosis (CAD) [23][24][25][26][27] is an advanced technology to help radiologists and oncologists to read and identify the nodules from the CT-scanned image of the patient. CAD can be categorized into image processing and machine

No abstract

No abstract