A convolutional neural network (CNN) for defect detection of additively manufactured parts

“Additive manufacturing (AM) is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. The convolutional neural network (CNN) has been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes. A network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the detection of good deposition quality and defects such as lack of fusion, gas porosity, and cracks in a fusion-based AM process, a CNN architecture is presented comparing the classification report and evaluation of different architectural settings and obtaining the optimized result from them. Since data set preparation, visualization, and balancing are very important aspects in deep learning to improve the performance and accuracy of neural network architectures, exploratory data analysis was performed for data visualization and the up-sampling method was implemented to balance the data set for each class. By comparing the results for different architectures, the optimal CNN network was chosen for further investigation. To tune the hyperparameters and to achieve an optimized parameter set, a design of experiments was implemented to improve the performance of the network. The performance of the network with optimized parameters was compared with the results from the previous study. The overall accuracy ( \u3e 97%) for both training and testing the CNN network presented in this work transcends the current state of the art (92%) for AM defect detection”--Abstract, page iv

딥러닝을 이용한 녹내장 진단 보조 시스템

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.본 논문에서는 딥 러닝 기반의 진단 보조 시스템을 제안하였다. 새로운 방법이 녹내장 데이터에 적용되었고 결과를 평가하였다. 첫번째 연구에서는 스펙트럼영역 빛간섭단층촬영기(SD-OCT)를 딥 러닝 분류 기를 이용해 분석하였다. 스펙트럼영역 빛간섭단층촬영기는 녹내장으로 인한 구조적 손상을 평가하기 위해 사용하는 장비이다. 분류 알고리즘은 합성 곱 신경망을 이용해 개발 되었으며, 스펙트럼영역 빛간섭단층촬영기의 망막신경섬유층(RNFL)과 황반부 신경절세포내망상층 (GCIPL) 사진을 이용해 학습했다. 제안한 방법은 두개의 이미지를 입력으로 받는 이중입력합성곱신경망(DICNN)이며, 딥 러닝 분류에서 효과적인 것으로 알려져 있다. 이중입력합성곱신경망은 망막신경섬유층 과 신경절세포층 의 두께 지도를 이용하여 학습 됐으며, 학습된 네트워크는 녹내장과 정상 군을 구분한다. 이중입력합성곱신경망은 정확도와 수신기동작특성곡선하면적 (AUC)으로 평가 되었다. 망막신경섬유층과 신경절세포층 두께 지도로 학습된 설계한 딥 러닝 모델을 조기 녹내장과 정상 군을 분류하는 성능을 평가하고 비교하였다. 성능평가 결과 이중입력합성곱신경망은 조기 녹내장을 분류하는데 0.869의 수신기동작특성곡선의넓이와 0.921의 민감도, 0.756의 특이도를 보였다. 두번째 연구에서는 딥 러닝을 이용해 시신경유두사진의 해상도와 대비, 색감, 밝기를 보정하는 방법을 제안하였다. 시신경유두사진은 녹내장을 진단하는데 있어 효과적인 것으로 알려져 있다. 하지만, 녹내장의 진단에서 환자의 나, 작은 동공, 매체 불투명성 등으로 인해 평가가 어려운 경우가 있다. 초 해상도와 보정 알고리즘은 초 해상도 적대적생성신경망을 통해 개발되었다. 원본 고해상도의 시신경 유두 사진은 저해상도 사진으로 축소되고, 보정된 고해상도 시신경유두사진으로 보정 되며, 보정된 사진은 시신경여백의 가시성과 근처 혈관을 잘 보이도록 후처리 알고리즘을 이용한다. 저해상도이미지를 보정된 고해상도이미지로 복원하는 과정을 초해상도적대적신경망을 통해 학습한다. 설계한 네트워크는 신호 대 잡음 비(PSNR)과 구조적유사성(SSIM), 평균평가점(MOS)를 이용해 평가 되었다. 현재의 연구는 딥 러닝이 안과 이미지를 4배 해상도와 구조적인 세부 항목이 잘 보이도록 개선할 수 있다는 것을 보여주었다. 향상된 시신경유두 사진은 시신경의 병리학적인 특성의 진단 정확도를 명확히 향상시킨다. 성능평가결과 평균 PSNR은 25.01 SSIM은 0.75 MOS는 4.33으로 나타났다. 세번째 연구에서는 환자 정보와 안과 영상(시신경유두 사진과 붉은색이 없는 망막신경섬유층 사진)을 이용해 녹내장 의심 환자를 분별하고 녹내장 의심 환자의 발병 연수를 예측하는 딥 러닝 모델을 개발하였다. 임상 데이터들은 녹내장을 진단하거나 예측하는데 유용한 정보들을 가지고 있다. 하지만, 어떻게 다양한 유형의 임상정보들을 조합하는 것이 각각의 환자들에 대해 잠재적인 녹내장을 예측하는데 어떤 영향을 주는지에 대한 연구가 진행 된 적이 없다. 녹내장 의 심자 분류와 발병 년 수 예측은 합성 곱 자동 인코더(CAE)를 비 지도적 특성 추출 기로 사용하고, 기계학습 분류 기와 회귀기를 통해 진행하였다. 설계한 모델은 정확도와 평균제곱오차(MSE)를 통해 평가 되었으며, 이미지 특징과 환자 특징은 조합했을 때 녹내장 의심 환자 분류와 발병 년 수 예측의 성능이 이미지 특징과 환자 특징을 각각 썼을 때보다 성능이 좋았다. 정답과의 MSE는 2.613으로 나타났다. 본 연구에서는 딥 러닝을 이용해 녹내장 관련 임상 데이터 중 망막신경섬유층, 신경절세포층 사진을 녹내장 진단에 이용되었고, 시신경유두 사진은 시신경의 병리학적인 진단 정확도를 높였고, 환자 정보는 보다 정확한 녹내장 의심 환자 분류와 발병 년 수 예측에 이용되었다. 향상된 녹내장 진단 성능은 기술적이고 임상적인 지표들을 통해 검증되었다.This paper presents deep learning-based methods for improving glaucoma diagnosis support systems. Novel methods were applied to glaucoma clinical cases and the results were evaluated. In the first study, a deep learning classifier for glaucoma diagnosis based on spectral-domain optical coherence tomography (SD-OCT) images was proposed and evaluated. Spectral-domain optical coherence tomography (SD-OCT) is commonly employed as an imaging modality for the evaluation of glaucomatous structural damage. The classification model was developed using convolutional neural network (CNN) as a base, and was trained with SD-OCT retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) images. The proposed network architecture, termed Dual-Input Convolutional Neural Network (DICNN), showed great potential as an effective classification algorithm based on two input images. DICNN was trained with both RNFL and GCIPL thickness maps that enabled it to discriminate between normal and glaucomatous eyes. The performance of the proposed DICNN was evaluated with accuracy and area under the receiver operating characteristic curve (AUC), and was compared to other methods using these metrics. Compared to other methods, the proposed DICNN model demonstrated high diagnostic ability for the discrimination of early-stage glaucoma patients in normal subjects. AUC, sensitivity and specificity was 0.869, 0.921, 0.756 respectively. In the second study, a deep-learning method for increasing the resolution and improving the legibility of Optic-disc Photography(ODP) was proposed. ODP has been proven to be useful for optic nerve evaluation in glaucoma. But in clinical practice, limited patient cooperation, small pupil or media opacities can limit the performance of ODP. A model to enhance the resolution of ODP images, termed super-resolution, was developed using Super Resolution Generative Adversarial Network(SR-GAN). To train this model, high-resolution original ODP images were transformed into two counterparts: (1) down-scaled low-resolution ODPs, and (2) compensated high-resolution ODPs with enhanced visibility of the optic disc margin and surrounding retinal vessels which were produced using a customized image post-processing algorithm. The SR-GAN was trained to learn and recognize the differences between these two counterparts. The performance of the network was evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Opinion Score (MOS). The proposed study demonstrated that deep learning can be applied to create a generative model that is capable of producing enhanced ophthalmic images with 4x resolution and with improved structural details. The proposed method can be used to enhance ODPs and thereby significantly increase the detection accuracy of optic disc pathology. The average PSNR, SSIM and MOS was 25.01, 0.75, 4.33 respectively In the third study, a deep-learning model was used to classify suspected glaucoma and to predict subsequent glaucoma onset-year in glaucoma suspects using clinical data and retinal images (ODP & Red-free Fundus RNFL Photo). Clinical data contains useful information about glaucoma diagnosis and prediction. However, no study has been undertaken to investigate how combining different types of clinical information would be helpful for predicting the subsequent course of glaucoma in an individual patient. For this study, image features extracted using Convolutional Auto Encoder (CAE) along with clinical features were used for glaucoma suspect classification and onset-year prediction. The performance of the proposed model was evaluated using accuracy and Mean Squared Error (MSE). Combing the CAE extracted image features and clinical features improved glaucoma suspect classification and on-set year prediction performance as compared to using the image features and patient features separately. The average MSE between onset-year and predicted onset year was 2.613 In this study, deep learning methodology was applied to clinical images related to glaucoma. DICNN with RNFL and GCIPL images were used for classification of glaucoma, SR-GAN with ODP images were used to increase detection accuracy of optic disc pathology, and CAE & machine learning algorithm with clinical data and retinal images was used for glaucoma suspect classification and onset-year predication. The improved glaucoma diagnosis performance was validated using both technical and clinical parameters. The proposed methods as a whole can significantly improve outcomes of glaucoma patients by early detection, prediction and enhancing detection accuracy.Contents Abstract i Contents iv List of Tables vii List of Figures viii Chapter 1 General Introduction 1 1.1 Glaucoma 1 1.2 Deep Learning for Glaucoma Diagnosis 3 1.4 Thesis Objectives 3 Chapter 2 Dual-Input Convolutional Neural Network for Glaucoma Diagnosis using Spectral-Domain Optical Coherence Tomography 6 2.1 Introduction 6 2.1.1 Background 6 2.1.2 Related Work 7 2.2 Methods 8 2.2.1 Study Design 8 2.2.2 Dataset 9 2.2.3 Dual-Input Convolutional Neural Network (DICNN) 15 2.2.4 Training Environment 18 2.2.5 Statistical Analysis 19 2.3 Results 20 2.3.1 DICNN Performance 20 2.3.1 Grad-CAM for DICNN 34 2.4 Discussion 37 2.4.1 Research Significance 37 2.4.2 Limitations 40 2.5 Conclusion 42 Chapter 3 Deep-learning-based enhanced optic-disc photography 43 3.1 Introduction 43 3.1.1 Background 43 3.1.2 Needs 44 3.1.3 Related Work 45 3.2 Methods 46 3.2.1 Study Design 46 3.2.2 Dataset 46 3.2.2.1 Details on Customized Image Post-Processing Algorithm 47 3.2.3 SR-GAN Network 50 3.2.3.1 Design of Generative Adversarial Network 50 3.2.3.2 Loss Functions 55 3.2.4 Assessment of Clinical Implications of Enhanced ODPs 58 3.2.5 Statistical Analysis 60 3.2.6 Hardware Specifications & Software Specifications 60 3.3 Results 62 3.3.1 Training Loss of Modified SR-GAN 62 3.3.2 Performance of Final Network 66 3.3.3 Clinical Validation of Enhanced ODP by MOS comparison 77 3.3.4 Comparison of DH-Detection Accuracy 79 3.4 Discussion 80 3.4.1 Research Significance 80 3.4.2 Limitations 85 3.5 Conclusion 88 Chapter 4 Deep Learning Based Prediction of Glaucoma Onset Using Retinal Image and Patient Data 89 4.1 Introduction 89 4.1.1 Background 89 4.1.2 Related Work 90 4.2 Methods 90 4.2.1 Study Design 90 4.2.2 Dataset 91 4.2.3 Design of Overall System 94 4.2.4 Design of Convolutional Auto Encoder 95 4.2.5 Glaucoma Suspect Classification 97 4.2.6 Glaucoma Onset-Year Prediction 97 4.3 Result 99 4.3.1 Performance of Designed CAE 99 4.3.2 Performance of Designed Glaucoma Suspect Classification 101 4.3.3 Performance of Designed Glaucoma Onset-Year Prediction 105 4.4 Discussion 110 4.4.1 Research Significance 110 4.4.2 Limitations 110 4.5 Conclusion 111 Chapter 5 Summary and Future Works 112 5.1 Thesis Summary 112 5.2 Limitations and Future Works 113 Bibliography 115 Abstract in Korean 127 Acknowledgement 130Docto

Quality control in clothing manufacturing with machine learning

Quality control is vital for business and machine learning has proven to be successful in this type of area. In this work we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning. The system consists of using pictures taken through mobile devices to detect defects on clothing items. A defect can be a missing component or a wrong component in a clothing item. Therefore, the function of system is to classify the objects that compose a clothing item through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all classes. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classifies features extracted from pictures of the clothing items using a convolutional neural network (CNN), which have proven to be very successfully in image classification problems. As the result of this work, we have developed a quality control system that combines a mobile application to take pictures of clothing items and a server that performs defect detection processes using an accurate image classification model capable of increasing its knowledge from new unseen data. This system can help factories improve their quality control processes.O controlo de qualidade é vital para um negócio e a aprendizagem automática tem provado ser bem-sucedida neste tipo de área. Neste trabalho propomos e desenvolvemos um sistema de controlo de qualidade para o fabrico de roupas utilizando aprendizagem automática. O sistema consiste em usar fotografias, tiradas através de dispositivos móveis, para detetar defeitos em peças de roupa. Um defeito pode ser a falta de um componente ou um componente errado numa peça de roupa. A função do sistema é, portanto, classificar os objetos que compõem uma peça de roupa através do uso de um modelo de classificação. À medida que um negócio fabril progride, novos objetos são criados, assim, o modelo de classificação deve ser capaz de aprender as novas classes sem perder conhecimento prévio. No entanto, a maioria dos algoritmos de classificação não suporta um aumento de classes, estes precisam ser treinados a partir do zero com todas as classes. Neste trabalho, utilizamos um algoritmo que suporta aprendizagem incremental para resolver este problema. Este algoritmo classifica características extraídas de imagens das peças de roupa usando uma rede neural convolucional, que tem provado ser uma técnica muito bem sucedida no que toca a resolver problemas de classificação de imagem. Como resultado deste trabalho, desenvolvemos um sistema de controlo de qualidade que combina uma aplicação móvel para tirar fotografias de peças de roupa e um servidor que executa os processos de deteção de defeitos usando um modelo de classificação de imagens preciso, capaz de aumentar o seu conhecimento a partir de novos dados nunca antes vistos. Este sistema pode ajudar as fábricas a melhorar seus processos de controlo de qualidade