Deep Quality: A Deep No-reference Quality Assessment System

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods

Deep Quality: A Deep No-reference Quality Assessment System

Image quality assessment (IQA) continues to garner great interestin the research community, particularly given the tremendousrise in consumer video capture and streaming. Despite significantresearch effort in IQA in the past few decades, the area of noreferenceimage quality assessment remains a great challenge andis largely unsolved. In this paper, we propose a novel no-referenceimage quality assessment system called Deep Quality, which leveragesthe power of deep learning to model the complex relationshipbetween visual content and the perceived quality. Deep Qualityconsists of a novel multi-scale deep convolutional neural network,trained to learn to assess image quality based on training samplesconsisting of different distortions and degradations such as blur,Gaussian noise, and compression artifacts. Preliminary results usingthe CSIQ benchmark image quality dataset showed that DeepQuality was able to achieve strong quality prediction performance(89% patch-level and 98% image-level prediction accuracy), beingable to achieve similar performance as full-reference IQA methods

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Application of deep learning general-purpose neural architectures based on vision transformers for ISIC melanoma classification

The field of computer vision has for years been dominated by Convolutional Neural Networks (CNNs) in the medical field. However, there are various other Deep Learning (DL) techniques that have become very popular in this space. Vision Transformers (ViTs) are an example of a deep learning technique that has been gaining in popularity in recent years. In this work, we study the performance of ViTs and CNNs on skin lesions classification tasks, specifically melanoma diagnosis. We compare the performance of ViTs to that of CNNs and show that regardless of the performance of both architectures, an ensemble of the two can improve generalization. We also present an adaptation to the Gram-OOD* method (detecting Out-of-distribution (OOD) using Gram matrices) for skin lesion images. A rescaling method was also used to address the imbalanced dataset problem, which is generally inherent in medical images. The phenomenon of super-convergence was critical to our success in building models with computing and training time constraints. Finally, we train and evaluate an ensemble of ViTs and CNNs, demonstrating that generalization is enhanced by placing first in the 2019 and third in the 2022 ISIC Challenge Live. Leaderboard (available at \href{https://challenge.isic-archive.com/leaderboards/live/}{https://challenge.isic-archive.com/leaderboards/live/})

Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis

This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector

Identification and Estimation of Clinical Indices Useful for the Diagnosis of Melanoma from Macroscopic Images

openMelanoma is an extremely aggressive form of skin cancer. When not promptly detected and treated, it can quickly metastasize, leading to unfavourable prognostic outcomes. Achieving early melanoma diagnosis relies heavily on accurate and thorough skin analysis, made by an expert dermatologist. To address subjective judgments and time-expensive exams, a novel screening and diagnostic method utilising photogrammetry-derived images of skin lesions has been devised. This innovative approach is based on the acquisition of macroscopic images, depicting a large portion of the patient body, and enables the creation of a three-dimensional model of the patient, allowing for the extraction of corresponding images of each individual lesion. This thesis aims to quantitatively assess the asymmetry, the irregularity of the border and the color of skin lesions through the analysis of segmented macroscopic images, contributing to the development of an automated diagnostic tool useful to the clinician for melanoma identification. The analysis was conducted on a dataset comprising images of healthy skin lesions and lesions reported as suspicious by dermatologists among which nine cases were confirmed as melanomas by biopsy. By utilizing algorithms to objectively compute asymmetry and border irregularity parameters, coupled with an in-depth analysis of color features associated with melanocytic lesions, the investigation unveiled statistically significant differences in these attributes between benign and suspicious lesions. Indeed, statistical tests confirmed distinctive distributions of these parameters between the two skin lesion populations. These findings underscore the potential of automated diagnostic tools derived from macroscopic images in effectively identifying suspicious lesions, thus contributing to early melanoma detection strategies

Generative Adversarial Network Image synthesis method for skin lesion Generation and classification

Skin cancer is the most commonly diagnosed cancer in today's growing population. One of the common limitations in the treatment of cancer is in the early detection of this disease. Mostly, skin cancer is detected in its later stages, when it has already compromised most of the skin area. Early detection of skin cancer is of utmost importance in increasing the chances for successful treatment, thus reducing mortality and morbidity. Currently, most dermatologists use a special microscope to examine the pattern and the affected area. This method is time-consuming and is prone to human errors, so there is a need for detecting skin cancer automatically. In this study, we investigate the automated classification of skin cancer using the Deep Convolution Generative Adversarial Network(DCGAN).In this work, Deep Convolutional GAN is used to generate realistic synthetic dermoscopic images, in a way that could enhance the classification performance in a large dataset and to evaluate whether the classification accuracy is enhanced or not, by generating a substantial amount of new skin lesion images. The DCGAN is trained using images generated by the Generator and then tweaked using the actual images and allow the Discriminator to make a distinction between fake and real images. The DCGAN might need slightly more fine-tuning to ripe a better return. Hyperparameter optimization can be utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters, namely number of iterations, batch size, and Learning rate can be tweaked, for example in this work we decreased the learning rate from the default 0.001 to 0.0002 and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models. Moreover, at each iteration in the course of the training process, the weights of the discriminative and generative network are updated to balance the loss between them. This pretraining and fine-tuning process is substantial for the model performance