Depth estimation from monocular images

This work will focus on studying different deep learning architectures for obtaining depth information from monocular RGB images.During this project, state-of-the-art deep learning models have been used to estimate depth maps from a monocular RGB image applying a teacher-student learning approach. This paradigm has been used in order to distillate the knowledge of high capacity deep neural networks into shallower ones to make inference faster for real-time applications. Some successful applications of this technique can be found both at natural language and computer vision applications

Incisor malocclusion using cut-out method and convolutional neural network

Malocclusion is a condition of misaligned teeth or irregular occlusion of the upper and lower jaws. This condition leads to poor performance of vital functions such as chewing. A common procedure in orthodontic treatment for malocclusion is a conventional diagnostic procedure where a dental health professional takes dental x-rays to examine the teeth to diagnose malocclusion. However, the manual orthodontic diagnostic procedure by dental experts to identify malocclusion is time-consuming and vulnerable to expert bias that results in delayed treatment completion time. Recently, artificial intelligence technology in image processing has gained attention in orthodontics treatment, accelerating the diagnosis and treatment process. However, several issues concerning the dental images as input of the classification model may affect the accuracy of the classification. In addition, unstructured images with varying sizes and the problem of a machine learning algorithm that does not focus on the region of interest (ROI) for incisor features bring challenges in delivering the treatment. This study has developed a malocclusion classification model using the cut-out method and Convolutional Neural Network (CNN). The cut-out method restructures the input images by standardising the sizes and highlighting the incisor sections of the images which assisted the CNN in accurately classifying the malocclusion. From the results, the implementation of the cut-out method generates higher accuracy across all classes of malocclusion compared to the non-implementation of the cut-out method

A comparative study of different pre-trained deeplearning models and custom CNN for pancreatic tumor detection

Artificial Intelligence and its sub-branches like MachineLearning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of computed tomography images,which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models werepreviously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular inthe medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per-forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project areVGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this applicationcan assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method

Efficient Weed Segmentation with Reduced Residual U-Net using Depth-wise Separable Convolution Network

482-494Selective weed treatment is a cost-effective method that reduces manpower and usage of the agrochemical, at the same time it requires an effective computer vision system to identify weeds and should be smaller in size to run in resource-constrained devices. To accomplish this, a convolution neural network named Reduced Residual U-Net using Depth-wise separable Convolution (RRUDC) network is proposed in this paper. Residual Depth-wise separable Convolution Block (RDCB) is introduced as a functional unit in both contractive and expanding paths. Residual connection is incorporated inside every RDCB unit. This network employs semantic segmentation to analyze the crop field images pixel-wise. To reduce the parameter size, a depth-wise separable convolution technique is used which curtail the number of parameters generated by the model at a ~1/9 ratio with a very negligible drop in accuracy. The model is trained using Crop Weed Field Image Dataset (CWFID) and then the trained model is pruned to reduce the model size further. It compresses the final model size by around ~70% without affecting the performance. It has achieved segmentation accuracy of ~96%, a lesser error rate with a model size less than 3 MB. It can be compatible with converting the proposed deep learning model into a real-time computer vision application that seems more convenient for farmers in their resource-constrained devices on their agricultural land

Magnetic resonance fingerprinting con reti neurali a valori complessi

In questo documento cerco un metodo per migliorare le prestazioni del MRF (Magnetic Resonance Fingerprinting), una tecnica di risonanza magnetica quantitativa. Il problema è quello di diminuire il tempo di calcolo necessario per determinare i parametri tissutali relativi alla risonanza magnetica effettuata. Il metodo proposto è quello dell'utilizzo di reti neurali a valori complessi con input il segnale di risonanza magnetica e con output i valori relativi ai parametri che si vogliono studiare. Dopo aver chiarito il concetto di risonanza magnetica, di MRF ed i problemi ad essi associati, introduco le reti neurali: l'architettura, la dinamica e l'apprendimento relativi ad esse. Discuto a seguire i problemi relativi all'introduzione dei numeri complessi nel modello di rete neurale e anche i vantaggi che le reti neurali a valori complessi possono portare, non solo rispetto ai metodi tradizionali, ma anche rispetto a reti neurali a valori reali. Analizzo inoltre delle tecniche utili a migliorare la generalizzazione e rendere le reti neurali a valori complessi una soluzione ancora più concreta. Studio quindi i miglioramenti introdotti dagli ensemble di reti neurali e dall'applicazione di funzioni d'attivazione stocastiche, che introducono del rumore gaussiano all'interno del modello