This research aims to improve the efficiency of skin lesion segment locations for the given input image of skin cancer using a combination of recently modified segmentation algorithms. Skin lesion segmentation is still a challenging task in medical image analysis because of the low contrast and high noise produced by dermoscopic imaging. Previous works extracted spatially‐oriented information but failed

Detection of brain tumor's grade is a very important task in treatment plan design which was done using invasive methods such as pathological examination. This examination needs resection procedure and resulted in pain, hemorrhage and infection. The aim of this study is to provide an automated non‐invasive method for estimation of brain tumor’s grade using Magnetic Resonance Images (MRI). After pre‐processing

Iris localization is the crucial link of iris recognition and automatic eye tracking. Based on the traditional Hough transform, this paper proposes an accurate pupil detection method combined with ellipse fitting and circular data compensation. We used the minimum gray mean method to approximately determine the inner edge. According to the results, the inner edge image is extracted and finely located

The application of deep transfer learning techniques has been successful in developing accurate systems for brain tumour classification on large‐scale medical image databases. For small databases, feature learning by deep neural networks is not robust. The systems based on domain‐specific hand‐crafted features have limited accuracy. In this paper, the authors focus on developing accurate models that

The use of anatomical multiatlas methods has proven to be one of the most competitive techniques for brain images segmentation. The majority of these methods are based on visual criteria of similarity between groups of an atlas to select a representative patient image to be segmented. However, this criterion is not necessarily linked to the performance of the segmentation. To overcome this dilemma

This article proposes an automatic method based on a combination of active shape model (ASM) and nonlinear support vector machine (SVM) accompanied with Bayesian information criterion which is utilized in order to measure radiomorphometric indices for digital X‐ray panoramic system. After omitting Poisson noise of input images, the image is divided. It attempts to choose suitable region of interest

Glaucoma ocular disease is the second topmost reason for irreversible visual impairment around the world. This malady can be cured and permanent blindness can be prevented by timely diagnosis and treatment. This study is an attempt to analyse the current status of automated glaucoma diagnosis systems. Existing systems have been analysed on the base of ophthalmic imaging technology, capability to detect

Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer‐aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based

Lung cancer is one of the most deadly cancer in both men and women. Accurate and early diagnosis of pulmonary lung nodules is critical. This study presents an accurate computer‐aided diagnosis (CADx) system for risk stratification of pulmonary nodules in computed tomography (CT) lung images by fusing shape and texture‐based features in a machine‐learning (ML) based paradigm. A database with 114 (28

To develop and validate an effective model for distinguishing COVID‐19 from bacterial pneumonia. In the training group and internal validation group, all patients were randomly divided into a training group (n = 245) and a validation group (n = 105). The whole lung lesion on chest computed tomography (CT) was drawn as the region of interest (ROI) for each patient. Both feature selection and model construction

Local binary patterns (LBPs) are used for effective texture representation in various applications. This study explores the clustering consistency and stability of image segmentation when distance‐based clustering methods are used with an LBP. Because data are described by features and attributes, distances among data are also dominated by the definition of features. Moreover, four popular LBP encoding

Recently, image processing schemes are widely used to improve disease detection performance in many medicinal fields. Cancer is considered as one of the most deadly disease and early diagnosis of cancer is the complicated task in the field of medicine. In this paper, we present the two pretrained convolutional neural network (CNN) based on ensemble models such as VGG19 and VGG16 for cancer diagnosis

Frequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so

The detection of brain tumors in brain magnetic resonance imaging (MRI) image is an important process for preventing earlier death. This article proposes an automated computer aided method for detecting and locating the brain tumors in brain MRI images using deep learning algorithms. The proposed method has three sub modules as preprocessing, classifications and segmentation. In this article, data

Currently, it remains one of the most challenging issues to distinguish brain functional networks of early mild cognitive impairment subjects (eMCIs) and normal control subjects (NCs). Unlike images, functional networks are non‐Euclidean data and not easily classified by dilated convolutional neural network (DCNN). To address this problem, we developed a sparse structure deep network embedding (SSDNE)

In today's world, liver cancers are one of the mainly popular cancers occurring in the human body. The greater part of liver carcinomas is more prone to alcohol‐related hepatitis and cirrhosis conditions. Moreover, there is another form of cancer namely, metastatic liver cancer, where the tumor is initiated from other organs and extends to the liver. Early and premature diagnosis of liver cancer is

Proliferative diabetic retinopathy is the advanced stage of diabetic retinopathy (DR) resulting in the growth of abnormal vessels on the retinal surface termed as neovascularization. This article primarily deals with the timely detection and classification of retinal images into healthy, neovascularization on optic disc, and elsewhere using an extreme learning machine (ELM) classifier. Initially, a

One of the largest organs of the human body is the skin and its pigmentation differs among the population. During skin disease identification, the dermatologist requires a high level of expertise and accuracy. This study proposes different kinds of skin image feature extraction and classification methods. In this work, we have chosen six kinds of skin diseases such as melanoma, seborrheic keratosis

Several studies have evaluated the usefulness of deep learning for lung segmentation using chest X‐ray (CXR) images with small‐ or medium‐sized abnormal findings. Here, we built a database including both CXR images with severe abnormalities and experts' lung segmentation results, and aimed to evaluate our network's efficacy in lung segmentation from these images. For lung segmentation, CXR images from

The frame rate improvement is an essential issue in dynamic ultrasonic imaging for better displaying rapid heart movements. In this study, a new technique using the compressive sensing (CS) theory was introduced for the frame rate improvement of two‐dimensional (2D) and three‐dimensional (3D) dynamic ultrasonic imaging. In the suggested procedure, a fewer radio frequency (RF) lines were received. Subsequently

The coronavirus disease (COVID‐19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID‐19. It is of great importance to rapidly and accurately segment COVID‐19 from CT to help diagnostic and patient monitoring. In this paper, we propose a U‐Net based segmentation network using attention mechanism. As not all the

The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique

Medical image fusion technology makes clinical diagnosis and treatment more accurate. This technique can solve the existing problem that single mode medical images conveying insufficient information. Therefore, the key to this technology is to retain as much as possible information in the original medical image of multiple modes. However, the existing methods often lose source image detail with a low

Antiscatter grids are used in biomedical X‐ray imaging to improve image quality by reducing scatter radiation reaching the image receptor. However, this comes at the cost of increasing radiation exposure. Grid performance can be improved by optimizing strip‐thickness, which reduces radiation exposure, leading to greater benefits achieved by the grid. Evidence has shown that strip height may also affect

The primary objective of this research work is to build a binary classifier for categorizing the input brain magnetic resonanceimaging (MRI) images as either demented or nondemented with high accuracy. A novel hyper‐parameter updating method called Randomized Adam (RanAdam) is proposed for enhancing the dementia classification accuracy of elephant herding optimization algorithm and other swarm intelligence

Medical image fusion focuses to fuse complementary diagnostic details for better visualization of comprehensive information and interpretation of various diseases and its treatment planning. In this paper, a multistage multimodal fusion model is presented based on nonsubsampled shearlet transform (NSST), stationary wavelet transform (SWT), and feature‐adaptive pulse coupled neural network. Firstly

High‐throughput synchrotron‐based tomographic microscopy at third generation light sources allows to probe cm‐sized samples at micrometer‐resolution. In this work, we present an approach to image a full mouse brain. With Indian‐ink as a contrast agent, it was possible to obtain 3D distribution of microvessels while a computational framework automatically extracted the morphological and geometrical

In this manuscript, a new approach using deep belief network (DBN) along opposition‐based hybrid grasshopper and honey bee optimization algorithm for lung cancer classification is proposed. Chest computed tomography (CT) is commonly used to diagnosis the lung tumors. Initially, the image quality is improved by preprocessing techniques, and then the features like texture, color and shape are extracted

Mass detection is a critical process in the examination of mammograms. The shape and texture of the mass are key parameters used in the diagnosis of breast cancer. To recover the shape of the mass, semantic segmentation is found to be more useful rather than mere object detection (or) localization. The main challenges involved in the mass segmentation include: (a) low signal to noise ratio (b) indiscernible

Liver ultrasound is a cost‐effective, non‐invasive, and sufficient technique to diagnose most of the liver disorders. The recent advancements in research in image processing have led to the development of image‐based liver disorder classification systems. In spite of being popular in the diagnostic imaging of liver, ultrasound images, owing to their poor quality, render the conventional and state of

Visual classification of pulmonary lesions from endobronchial ultrasonography (EBUS) images is performed by radiologists; therefore, results can be subjective. Here, two robust features, called the adaptive weighted‐sum of the upper triangular gray‐level co‐occurrence matrix (GLCM) and the adaptive weighted‐sum of the lower triangular GLCM (AWSL), were combined with 22 other standard features and used

This paper suggests a new technique for trabecular bone characterization using fractal analysis of X‐Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box‐counting method (DBCM) to estimate the fractal dimension

Melanoma is an early stage of skin cancer. The objective of the proposed work is to detect the symptoms of melanoma early through images of the moles obtained from image processing device and classify the types. The procedure involves converting raw melanoma skin image initially into hue, saturation, and intensity for digital processing. The required information for detecting melanoma is available

This paper aimed to evaluate the effect of various electrode configurations on applying a convolutional neural network (CNN) to recognize uterine contraction (UC) with Electrohysterogram (EHG) signals. Seven 8‐electrode configurations and thirteen 4‐electrode configurations were selected from the 4 × 4 electrode grid in the Icelandic 16‐electrode EHG database. EHG signals were divided into UC and non‐UC

Multiple sclerosis (MS) is a degenerative disease of the covering around the nerves in the central nervous system. It damages the immune cells and causes small lesions in the patient's brain. Automated image recognition techniques can be employed for increasing the accuracy of detection. The use of convolutional neural networks (CNN) is the most common deep learning method for detecting lesions in

Recently the increased utilization of computer‐aided detection is more helpful to assist the radiologist, recognize the minute lung lesion. In this research article, we propose three novel block detection methods for lung lesion segmentation. First, an automatic algorithm for lung lesion detection and segmentation by using histogram‐based affine‐invariant detection (HAID) is proposed. HAID is a novel

Diabetic Retinopathy is a retinal abnormality which is characterized by progressive damage to the retina, eventually leading to irreversible blindness. In this paper, we propose an aggregated residual transformation‐based model for automatic multistage classification of diabetic retinopathy. The proposed model obtains 99.68% overall classification accuracy, 99.68% sensitivity, 99.89% specificity and

We investigated whether a convolutional neural network (CNN) can enhance the usability of computer‐aided detection (CAD) of chest radiographs for various pulmonary abnormal lesions. The numbers of normal and abnormal patients were 6055 and 3463, respectively. Two radiologists delineated regions of interest for lesions and labeled the disease types as ground truths. The datasets were split into training

Diabetic retinopathy (DR) accounts in eye‐related disorders due to accumulated damage to small retinal blood vessels. Automated diagnostic systems are effective in early detection and diagnosis of severe eye complications by assisting the ophthalmologists. Deep learning‐based techniques have emerged as an advancement over conventional techniques based on hand‐crafted features. The authors have proposed

Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models.” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach

Fundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state‐of‐the‐art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation

Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions

A computer‐aided detection (CADe) scheme, relying on a large number of high‐quality images with annotations, could help radiologists effectively detect lung nodules. However, such medical data are generally difficult to obtain. To address this issue, this paper proposes a novel method based on a conditional generative adversarial network (CGAN) to generate new samples for data augmentation (DA). This

This paper aims to enhance the image quality in ultrasound images. The significant difficulties in the ultrasound image are the presence of speckle noise. Speckle is the granular noise, and this kind of noise produces a lot of challenges during medical diagnosis. Also, these kinds of problems degrade image quality. The proposed work overcomes this kind of issue with nonsubsampled contourlet domain‐based

In order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the

In this paper, we demonstrate that algebraic topology can be used to perform 2D+t object detection. After the construction of a topological complex for a 2D+t image sequence, we build a nested sequence of cell complexes on which relative persistent homology is computed. The relative homology adds to “absolute” homology the computation of classes related to the first and last frames of the sequence

The survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either

Segmentation of the human eye retinal image is an essential step for proper examination and diagnosis by the ophthalmologists or eye care specialists. A technique for vessel segmentation of retinal images is proposed. Retinal images are mostly low‐light images, which are first processed for enhancement of light as well as for detail amplification. Illumination of low‐light images is enhanced, and details

Pneumonia is one of the most common and fatal diseases in the world. Early diagnosis and treatment are important factors in reducing mortality caused by the aforementioned disease. One of the most important and common techniques to diagnose pneumonia disease is the X‐ray images. By evaluating these images, various machine‐learning methods are used for accuracy in diagnosis. The presented study in this

The early detection of tumor is necessary to save a number of lives. In women, the temperature of the affected area of the tumor is warmer than the unaffected area; therefore the thermography technique can be used to capture the cancerous breast images with a thermal infrared by identifying the temperature difference between them. Color enhancement of the captured breast image is an important consideration

In MRI, the image with poor quality, especially the image with noise interference or low contrast, may provide insufficient data for the visual interpretation of the affected part. Image segmentation provides an effective method to facilitate early detection and further diagnosis. By introducing a Particle Swarm Optimization (PSO) initialization step and a novel dissimilarity measure metric, we present

The brain tumor detection is a highly complicated but significant task. The early detection of a brain tumor can increase the survival rate of an individual by providing proper treatment. This work proposes a computer‐aided diagnostic method for brain tumor detection using fractional wavelet transform (FrDWT) with different values of alpha (α) ranging from (0.1‐1), histogram‐based various local feature

In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented

The high mortality rate associated with brain tumors requires early detection in the early stages to treat and reduce mortality. Due to the complexity of brain tissue, manual diagnosis of the brain and tumor tissues is very time‐consuming and operator dependent. Furthermore, there is a need for experts who can review the images to detect these effects, rendering traditional methods inefficient in their

Signal processing techniques incorporated with data compression processes enrich the signals and boost up storage efficiency and transmission reliability. Transmitting uncompressed original data consume wide bandwidth, which increases transmission time and leads to data hammering. These limitations enforce to look for strategic data compression techniques. Lossless compression techniques are requisite

Early detection and proper screening are essential to prevent vision loss due to glaucoma. In recent years, convolutional neural network (CNN) has been successfully applied to the color fundus images for the automatic detection of glaucoma. Compared to the existing automatic screening methods, CNNs have the ability to extract the distinct features directly from the fundus images. In this paper, a deep

DBT reconstructs high‐resolution tomographic images through multi‐angle scanning. We aimed at investigating the diagnostic value of DBT in breast microcalcifications of Asian women. The clinical characteristics and diagnostic accuracy of FFDM and DBT in 70 breast cancer patients were compared. 52 malignant lesions and 24 benign lesions were found in 76 breast microcalcifications. FFDM presented with

Melanoma is one of the most dangerous types of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this study, a system is proposed to detect melanoma automatically using an ensemble approach, including convolutional neural networks (CNNs) and image texture feature extraction. Two CNN models, a proposed

The heart is one of the most important and sophisticated organ of the human body. Coronary ischemia is a condition in which the coronary muscles do not receive sufficient blood and oxygen because of blocked or tightened heart vessels. This syndrome is called cardiac vessel illness. There have been numerous attempts to detect the impact of cardiac vessel illness on the heart muscles using noninvasive