Special Issue "Recent Advances in Deep Learning for Medical Image Analysis"

Submission Deadline: 01 April 2021 (closed)
Guest Editors
Dr. Tallha Akram, COMSATS University Islamabad, Pakistan.
Prof. Dr. Yu-Dong Zhang, University of Leicester, UK.
Prof. Dr. Robertas Damaševičius, Kaunas University of Technology, Lithuania.
Dr. Muhammad Attique Khan, HITEC University Taxila, Pakistan.

Summary

Artificial intelligence showed a huge interest, especially in the area of medical imaging from the last three years. Due to the spread of medical imaging modalities such as Magnetic Resonance Imaging (MRI), Ultrasound, and Positron Emission Tomography (PET), Dermoscopic images, X-Ray images, Mammograms, and histological images, enormous amounts of data are being generated related to these medical domains. The data is generated in the form of some images and related to health informatics. However, the amount of this data is too large and difficult to use by employing classical techniques (i.e. hand crafted features). The question is that how we can use this big amount of biomedical data to build the automated system with better accuracy and less computational time. Also, how we can utilize this data to develop an automated system for better diagnosis of cancers such as brain tumor, skin cancer, lung cancer, stomach cancer, COVID19 infected patients, and breast cancer. To handle the large amount of biomedical data, researchers of computer vision used deep learning. However, they facing several issues (i.e. high data dimensionality and imbalanced datasets) and to these issues, the performance of system were degraded. Therefore, the most of existing solutions are based on the balanced datasets which is not a good option for the multiclass classification problem. Therefore, it is essential to develop some advanced deep learning techniques. Also, it is required to develop dimensionality reduction techniques to minimize the prediction time. Also, the less prediction time can be useful for real-time computerized system.


Keywords
The aim of this special issue is to provide a diverse, but complementary set of solutions using deep learning for medical images. The solutions cover the above mentioned issues. We would also like to accept the new solutions but not limited to the following:
• Deep learning based features extraction for medical images
• Visualization of deep learning features for medical images
• Features selection using heuristic techniques for medical images
• Features selection using met heuristic techniques
• Deep learning features fusion
• Deep learning based biomedical images information fusion
• Transfer learning in medical imaging
• Features reduction techniques
• Theoretical analysis of deep learning for medical images
• Deep learning based segmentation of infected regions
• Semi-Supervised deep learning for medical imaging
• Semantic Segmentation for medical image analysis
• Multitask Learning for medical image analysis

Published Papers
  • Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework
  • Abstract Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation… More
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  • Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning
  • Abstract This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and… More
  •   Views:429       Downloads:405        Download PDF

  • Ensembles of Deep Learning Framework for Stomach Abnormalities Classification
  • Abstract

    Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More

  •   Views:362       Downloads:267        Download PDF

  • Malaria Parasite Detection Using a Quantum-Convolutional Network
  • Abstract

    Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected malaria cells. Then, the… More

  •   Views:290       Downloads:209        Download PDF

  • VISPNN: VGG-Inspired Stochastic Pooling Neural Network
  • Abstract Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image).… More
  •   Views:266       Downloads:216        Download PDF

  • A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification
  • Abstract Melanoma, due to its higher mortality rate, is considered as one of the most pernicious types of skin cancers, mostly affecting the white populations. It has been reported a number of times and is now widely accepted, that early detection of melanoma increases the chances of the subject’s survival. Computer-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning techniques. In this work, we propose a framework that accurately segments, and later classifies, the lesion using improved image segmentation and fusion methods. The proposed technique takes an image and passes it through two… More
  •   Views:309       Downloads:216        Download PDF

  • A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification
  • Abstract Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can… More
  •   Views:363       Downloads:249        Download PDF

  • COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis
  • Abstract Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and… More
  •   Views:425       Downloads:289        Download PDF

  • A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19
  • Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal… More
  •   Views:422       Downloads:365        Download PDF

  • Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic
  • Abstract During COVID-19, the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand. This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals, pharmacies, and retail stores as its customers. Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers. A questionnaire has been… More
  •   Views:658       Downloads:362        Download PDF

  • A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion
  • Abstract Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized… More
  •   Views:401       Downloads:295        Download PDF

  • Malaria Blood Smear Classification Using Deep Learning and Best Features Selection
  • Abstract Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is proposed, which takes images of… More
  •   Views:351       Downloads:268        Download PDF

  • Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI
  • Abstract For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental… More
  •   Views:433       Downloads:305        Download PDF

  • Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis
  • Abstract (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way data augmentation is chosen… More
  •   Views:359       Downloads:323        Download PDF

  • Classification of Retroviruses Based on Genomic Data Using RVGC
  • Abstract Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms. AIDS, cancer, neurologic disorders, and other clinical conditions can all be fatal due to retrovirus infections. Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods. The National Center for Biotechnology Information (NCBI) promotes science and health by making biomedical and genomic data available to the public. This research aims to classify the different types of rotavirus genome sequences available at the NCBI. First, nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage. Based on… More
  •   Views:388       Downloads:274        Download PDF

  • Screening of COVID-19 Patients Using Deep Learning and IoT Framework
  • Abstract In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID-19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we implement a cloud platform for… More
  •   Views:456       Downloads:298        Download PDF

  • An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification
  • Abstract Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape of a tumor, location of… More
  •   Views:668       Downloads:574        Download PDF

  • An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network
  • Abstract The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography (CT), where any chest anomalies (e.g., lung inflammation) can be easily identified. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artificial intelligence (AI) techniques have been developed… More
  •   Views:632       Downloads:526        Download PDF

  • Breast Lesions Detection and Classification via YOLO-Based Fusion Models
  • Abstract With recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images, then recognizes abnormal regions as… More
  •   Views:1033       Downloads:1045        Download PDF

  • Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset
  • Abstract Electroencephalography is a common clinical procedure to record brain signals generated by human activity. EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications, but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience. Various EEG analysis and classification techniques have been proposed to address this problem however, the conventional classification methods require identification and learning of specific EEG characteristics beforehand. Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification. One of the great implementations of deep learning is Convolutional Neural Network… More
  •   Views:802       Downloads:858        Download PDF

  • Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images
  • Abstract Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation… More
  •   Views:597       Downloads:645        Download PDF

  • Segmentation and Classification of Stomach Abnormalities Using Deep Learning
  • Abstract An automated system is proposed for the detection and classification of GI abnormalities. The proposed method operates under two pipeline procedures: (a) segmentation of the bleeding infection region and (b) classification of GI abnormalities by deep learning. The first bleeding region is segmented using a hybrid approach. The threshold is applied to each channel extracted from the original RGB image. Later, all channels are merged through mutual information and pixel-based techniques. As a result, the image is segmented. Texture and deep learning features are extracted in the proposed classification task. The transfer learning (TL) approach is used for the extraction… More
  •   Views:748       Downloads:638        Download PDF