We propose two different methods for image segmentation with the objective of marking contaminated regions in images from biochemical tests. The contaminated regions consists of thin hair or fibers and the purpose of this thesis is to eliminate the tedious task of masking the contaminated regions by hand by implementing automatic hair masking. Initially an algorithm based on Morphological Image Processing is presented, followed by solving the problem of pixelwise classification using a Convolutional Neural Network (CNN). Finally, the performance of each implementation is measured by comparing the segmented images with labelled images which are considered to be the ground truth. The result shows that both implementations have strong potential at successfully performing semantic segmentation on the images from the biochemical tests.

Hand pose estimation is a computer vision challenge that consists of detecting the coordinates of a hand’s key points in an image. This research investigates several deep learning-based solutions to determine whether or not it is possible to improve current state-of-the-art detectors for smartphone applications. Several models are tested and compared based on accuracy, processing speed and memory size. A final network is selected and detailed to compare it to the state-of-the-art. The proposed solution is obtained by combining the Differentiable Spatial to Numerical Transform layer to predict numerical coordinates together with the Fire module presented in the SqueezeNet architecture. This deep neural network contains around 1 million parameters and is able to outperform the current best documented model in all the metrics described above. A qualitative analysis is also performed to examine the predictions of the final solution on test images. / Att bestämma en hands orientering är en utmaning inom bildanalys som består i att detektera koordinaterna för olika nyckelpunkter för handen i en bild. I denna studie undersöks ett antal metoder baserade på djupinlärning för att avgöra huruvida det är möjligt att förbättra existerande detektorer för tillämpningar på smartphones. Flera olika modeller testas och jämförs baserat på noggrannhet, beräkningshastighet och minneskrav. Ett slutligt nätverk väljs, analyseras och jämföras med nuvarande state-of-the-art teknik. Den lösning som föreslås erhålls genom att kombinera ett så kallat Differentiable Spatial to Numerical Transform-lager, för att förutsäga numeriska koordinater, tillsammans med en så kallad Fire-modul som tidigare presenteras som en del av arkitekturen SqueezeNet. Detta djupa neurala nätverk innehåller cirka en miljon parametrar och kan överträffa den nuvarande mest dokumenterade modellen i alla de avseenden som beskrivits ovan. En kvalitativ analys utförs också för att undersöka den slutliga lösningens uppskattningar på testbilder.

Att träna neurala nätverk tar mycket tid och kan kräva extrema mängder data. Både träningstiden och mängden data som behövs kan minskas med transfer learning. I detta examensarbete studeras effekterna av transfer learning när ett neurala nätverk tränas på en liten datamängd. VGG16, MobileNeV3 och SqeezeNet används som förtränade modeller. Modellerna modifierades för att passa den nya datasetet. Ytterligare modifieringar gjordes för att testa om det kunde förbättra generaliseringen och minska träningstiden. Experimenten visade att transfer learning kan minska träningstiden och resulterade i modeller med bättre generalisering än slumpmässigt initialiserade modeller. Experimenten visade också att en modifierad version av SqeezeNet är den mest framgångsrika modellen. / Training neural networks takes a lot of time and can require extreme amounts of data. Both training time and the amount of data needed can be reduced with transfer learning. In this thesis the effects of transfer learning are studied when training a neural network on a small dataset. VGG16, MobileNeV3 and SqeezeNet are used as pre-trained models. The models were modified to fit the new dataset. Further modifications were made to test whether it could improve the generalization and reduced training time. The experiments showed that transfer learning can lead to shorter training time and resulted in models with better generalization than random initialized models. The experiments also showed that a modified version of SqeezeNet is the most successful model.

Typically, inverse imaging problems deal with the reconstruction of images from the sensor measurements where sensors can take form of any imaging modality like camera, radar, hyperspectral or medical imaging systems. In an ideal scenario, we can reconstruct the images via applying an inversion procedure from these sensors’ measurements, but practical applications have several challenges: the measurement acquisition process is heavily corrupted by the noise, the forward model is not exactly known, and non-linearities or unknown physics of the data acquisition play roles. Hence, perfect inverse function is not exactly known for immaculate image reconstruction. To this end, in this dissertation, I propose an automatic sensing and reconstruction scheme based on deep learning within the compressive sensing (CS) framework to solve the computational imaging problems. Here, I develop a data-driven approach to learn both the measurement matrix and the inverse reconstruction scheme for a given class of signals, such as images. This approach paves the way for end-to-end learning and reconstruction of signals with the aid of cascaded fully connected and multistage convolutional layers with a weighted loss function in an adversarial learning framework. I also propose to extend our analysis to introduce data driven models to directly classify from compressed measurements through joint reconstruction and classification. I develop constrained measurement learning framework and demonstrate higher performance of the proposed approach in the field of typical image reconstruction and hyperspectral image classification tasks. Finally, I also propose a single data driven network that can take and reconstruct images at multiple rates of signal acquisition. In summary, this dissertation proposes novel methods on the data driven measurement acquisition for sparse signal reconstruction and classification, learning measurements for given constraints underlying the requirement of the hardware for different applications, and producing a common data driven platform for learning measurements to reconstruct signals at multiple rates. This dissertation opens the path to the learned sensing systems. The future research can use these proposed data driven approaches as the pivotal factors to accomplish task-specific smart sensors in several real-world applications.

Muscular rehabilitation is essential for injury or surgery recovery by restoring strength, flexibility, and range of motion to the affected joints and muscles. It can also improve posture correction and performance by strengthening weak areas, reducing the risk of injury, and managing chronic conditions like arthritis, osteoporosis, or chronic pain. Currently, there is only physical therapy for these problems, and the treatment is in person at a specific location, such as a hospital or a clinic. Other works proposed mounting surface electromyography to recognize muscle activation patterns or wrist-forearm for muscle fatigue or using cameras to video call for sessions. Regrettably, such works put the patient’s privacy or comfort in danger. Our proposed solution is a radar and machine learning-based monitoring and classification of rehabilitation exercises. This RF-based system can accurately monitor and classify exercises that are part of the treatment for a specific need and in the privacy of the patient’s house. The proposed solution uses the RF reflections of the body and the environment. It uses these signals to analyze them in a machine learning algorithm to classify the exercise the person executes. This solution could be used anywhere in the home by any patient with minimal setup effort. Our results, done by four subjects in their own homes, show that the already trained model can classify with an accuracy of 87% to 97%.

Breast cancer is the second fatal disease among cancers both in Canada and across the globe. However, in the case of early detection, it can raise the survival rate. Thus, researchers and scientists have been practicing to develop Computer-Aided Diagnosis (CAD)x systems. Traditional CAD systems depend on manual feature extraction, which has provided radiologists with poor detection and diagnosis tools. However, recently the application of Convolutional Neural Networks (CNN)s as one of the most impressive deep learning-based methods and one of its interesting techniques, Transfer Learning, has revolutionized the performance and development of these systems. In medical diagnosis, one issue is distinguishing between breast mass lesions and calcifications (little deposits of calcium). This work offers a solution using transfer learning and ensemble learning (majority voting) at the first stage and later replacing the voting strategy with soft voting. Also, regardless of the abnormality's type (mass or calcification), the severeness of the abnormality plays a key role. Nevertheless, in this study, we went further and made an effort to create a (CAD)x pathology diagnosis system. More specifically, after comparing multi-classification results with a two-staged abnormality diagnosis system, we propose the two-staged binary classifier as our final model. Thus, we offer a novel breast cancer diagnosis system using a wide range of pre-trained models in this study. To the best of our knowledge, we are the first who integrate the application of a wide range of state-of-the-art pre-trained models, particularly including EfficientNet for the transfer learning part, and subsequently, employ ensemble learning. With the application of pre-trained CNN-based models or transfer learning, we are able to overcome the lack of large-size datasets. Moreover, with the EfficientNet family offering better results with fewer parameters, we achieved promising results in terms of accuracy and AUC-score, and later ensemble learning was applied to provide robustness for the network. After performing 10-fold cross-validation, our experiments yielded promising results; while constructing the breast abnormality classifier 0.96 ± 0.03 and 0.96 for accuracy and AUC-score, respectively. Similarly, it resulted in 0.85 ± 0.08 for accuracy and 0.81 for AUC-score when constructing pathology diagnosis.

The aim of explainable machine learning is to aid humans in understanding how exactly complex machine learning models work. Machine learning models have offered great performance in various areas. However, the mechanisms behind how the model works and how decisions are being made remain unknown. This specific constraint increases the user’s hesitation to trust the results of the model and even to improve their performance further. Counterfactual explanation is one method to offer explainability in machine learning by indicating what would have happened if the input of a model was modified in a specific way. Fraud is the action of acquiring something from someone else in a dishonest manner. Companies’ and organizations’ vulnerability to malicious actions has been increasing due to the development of digitalization. Machine learning applications have been successfully put in place to tackle fraudulent actions. However, the severity of the impact of fraudulent actions has highlighted the need for further scientific exploration of the topic. The current research will attempt to do so by studying counterfactual explanations related to fraud detection. Latent-CF is a method for counterfactual generation that utilizes an autoencoder and gradient descent in its latent space. LatentCF++ is an extension of Latent-CF. It utilizes a classifier and an autoencoder. The aim is to perturb the encoded latent representation through a gradient descent optimization for counterfactual generation so that the initially undesired class is then classified with the desired prediction. Compared to Latent-CF, LatentCF++ uses Adam optimization and adds further constraints to ensure that the generated counterfactual’s class probability surpasses the set decision boundary. The research question the current thesis addresses is: “To what extent can LatentCF++ provide reliable counterfactual explanations in fraud detection?”. In order to provide an answer to this question, the study is applying an experiment to implement a new application of LatentCF++. The current experiment utilizes a onedimensional convolutional neural network as a classifier and a deep autoencoder for counterfactual generation in fraud data. This study reports satisfying results regarding counterfactual explanation production of LatentCF++ on fraud detection. The classification is quite accurate, while the reconstruction loss of the deep autoencoder employed is very low. The validity of the counterfactual examples produced is lower than the original study while the proximity is lower. Compared to baseline models, k-nearest neighbors outperform LatentCF++ in terms of validity and Feature Gradient Descent in terms of proximity.

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The aim of this work is to develop efficient named entity recognition from the given text that in turn improves the performance of the systems that use natural language processing (NLP). The performance of IoT-based devices such as Alexa and Cortana significantly depends upon an efficient NLP model. To increase the capability of the smart IoT devices in comprehending the natural language, named entity recognition (NER) tools play an important role in these devices. In general, the NER is a two-step process that initially the proper nouns are identified from text and then classify them into predefined categories of entities such as person, location, measure, organization and time. NER is often performed as a subtask while processing natural languages which increases the accuracy level of a NLP task. In this paper, we propose deep neural network architecture for named entity recognition for the resource-scarce language Hindi, based on convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM) neural network and conditional random field (CRF). In the proposed approach, initially, we use skip-gram word2vec model and GloVe model to represent words in semantic vectors which are further used in different deep neural network-based architectures. In the proposed approach, we use character- and word-level embedding to represent the text that includes information at fine-grained level. Due to the use of character-level embeddings, the proposed model is robust for the out-of-vocabulary words. Experimental results show that the combination of Bi-LSTM, CNN and CRF algorithms performs better as compared to the other baseline methods such as recurrent neural network, long short-term memory and Bi-LSTM individually.

Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutional neural networks. First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement neural networks and convolutional neural networks over encrypted data and measure performance of the models.