European Journal of Molecular & Clinical Medicine

The abnormal growth of skin cells is skin cancer. Early skin cancer diagnosis is important. The dermoscopic images of the watershed algorithm are presented for the detection of skin cancer. The Gaussian philtre eliminates unnecessary areas of the skin. Then normal and abnormal pictures of the pre-processed skin cancer segment are provided to the water-shifting algorithm. The findings demonstrate the efficacy of a method for classifying skin cancer using a watershed algorithm.

Segmenting brain tumors is a major challenge in the production of scientific pictures. To order to maximize care outcomes and increasing the hospital success rate, early detection of brain tumors plays an important part. A challenging and time-consuming job is the manual segmentation of brain tumors from large quantities of MRI images produced in clinical routine. Automatic brain tumor segmentation is possible. This article aims to analyze strategies for the segmentation of brain tumors dependent on MRI. Automatic segmentation using deep learning approaches has recently been proven common because these approaches accomplish the latest findings much better than other methods would solve this issue. Deep learning approaches may also provide for effective analysis and unbiased interpretation of vast volumes of picture evidence dependent on MRI. There are many papers on MRI based brain tumor segmentation which focus on traditional methods. Different from others, we concentrate on the recent trend in the field of deep learning. Next, the brain tumors and techniques for segmenting the brain tumor are added. Then, the new architectures are explored with a emphasis on the current development in deep learning methods. Finally, an evaluation is introduced and further improvements are discussed to standardize brain tumor segmentation procedures dependent on MRI in the day-to-day clinical practice.

Background and objectives: The upper and lower airway has always been an area of interest because the oropharyngeal and nasopharyngeal structures play important roles in the growth and development of the craniofacial complex. Significant relationships between the pharyngeal structures and both dento-facial and craniofacial structures have been reported. The aim of the study were to evaluate Oropharyngeal and nasal passage volumes of patients with normal nasorespiratory functions having different dentofacial skeletal patterns using CBCT and the correlations between different variables and the airway Material and Methods: The study consisted of 45 patients (23 males, 22 females), divided into 3 equal groups as Class I , Class II and Class III based on evaluation of facial profile and molar relation. After obtaining CBCT, the Oropharyngeal airway volume (OPV), Nasopharyngeal airway volume (NPV), vertical height of oropharynx (HOP), Constricted minimum axial area (CMinAx), and Constricted posterior airway space (CPAS) were measured. Differences between groups were determined by using the Tukey Post Hoc test. Correlations between the variables were tested with the Pearson's correlation coefficient. Results: The mean OPV of the Class II subjects (6876.40 mm3) was significantly lower when compared with that of the Class I (8294.73 mm3 ) and Class III subjects (10941.43 mm3 ). The only statistically significant difference for NPV was observed between the Class I (9889.57 mm3) and Class II groups (7916.48 mm3 ). The CMinAx had a high potential in explaining the OPV.Conclusion: The results from this study indicate that mandibular growth deficiency patients had less airway volume, minimum axial area and constricted posterior airway space than the patients with good growth anteroposterior relationship between maxilla and mandible. The results of this research can be used as a guideline for subsequent works related to the airway study and presurgical assessment for orthognathic surgeries

The medical diagnosis and clinical practice greatly demands medical image classification, an emerging area of research which includes modern medical imaging technology. Recently, Fuzzy Bat Algorithm (FBA) with Mean Weight Convolution Neural Network (MWCNN) algorithm was proposed for Region of Interest (RoI) area detection in the lung images in order to increase the classification accuracy. The image processing system outcomes are influenced by edge detection e.g. region segmentation, objects detection. Edge detection is done through Blended Kernel Based Fuzzy Local Information C-Means (BKFLICM) technique and construction of gradients in the scale is achieved by clustering of all image pixels in a feature space. The image segmentation mainly relies on the pixel intensity which is used for assessing resemblance amidst pixels. The edge detection using BKFLICM is performed by formation of new kernel range which is obtained by merging hyperbolic tangent kernel and Gaussian kernel. The special feature of BKFLICM is the fuzzy local (gray level) similarity measure through the kernel function. This does the edge detection perfectly while preserving the image details following which FBA and MWCNN classifier are utilized for segmentation and classification respectively. The training of lung image classification deprived of severe over-fitting is mainly done through MWCNN with sufficient labelled images and improved accuracy is also obtained for (LIDC-IDRI) database. The performance metrics such as accuracy, precision, recall, and F-measure values are also enhanced using the proposed algorithm which is validated by the experimental outcomes.

In this paper, a proposed hybrid algorithm using K-means and Fuzzy logic for brain segmentation, is developed, simulated and evaluated. The system identifies the white matter, gray matter and Cerebrospinal Fluid (CSF). The proposed system was tested using Magnetic Resonance Imaging (MRI), and evaluated in terms of the misclassification rate and percentage of clustering. The misclassification rate was found to be lesser in the proposed system as compared to the existing systems using K-means and Fuzzy logic. Further, the percentage of clustering is improved by the proposed system as compared to the existing algorithms. This work paves the way for future development of Neuro Fuzzy K-means algorithm in order to reduce the misclassification rate further in clustering the white matter, gray matter and CSF.

Almost all over the world, the economy mainly depends on the production of food.
Computer vision technology plays a pivotal role in the field of agriculture. The dream of this
research is to produce successful crops in the agricultural sector. Successful farming can
increase crop production in terms of both quality and quantity. The farming performs eight
major phases which begin from crop selection to harvesting. At any of these phases, the
disease and pest may destroy plants. However, the leaves are found to be the most damaged
part in disease identification. A lot of articles are taken out for the survey that endorses the
mechanism of image processing and deep learning for the detection and classification of
diseases from the crop leaves. This survey provides an overview of the pros and cons of all
such articles on various research aspects. The effectiveness of state-of-the-art methods is
explored to identify the techniques that seem to work well across different crops. This paper
indicates that algorithms like Support Vector Machine and Neural Network play an important
role in the crop disease identification and classification.

Lung cancer is also the most serious illness of the day for smokers. Small cell lung cancer
(SCLC) is the fatal type of lung cancer. This days, tumour detection is getting difficult. It is only
in the final stage that this form of lung cancer can be detected. Computer assisted identification
(CAD) and diagnosis mechanisms for lung cancer are an essential indicator of lung
segmentation, as the execution of those mechanisms is based on the execution of computed
tomography (CT) lung segmentation images. Image recognition system is commonly used for
early identification and treatment. Lung cancer prediction, hereditary cell identification and
environmental factors are essential factors in the development of lung cancer prevention
strategies. By predicting the movement of tumour cell it will be easier to control the tumour
spreading. This can be achieved to decrease the growth of the tumour cells using the motion
prediction model. So this paper contrasts the methods used at the earlier level to identify lung
tumors.

Automatic plant leaf curl detection is an important step towards the development of Computer-aided crop damage analysis systems. It helps in analyzing the health condition of the plants through leaf images. Image processing techniques are recently being used to analyze the condition of the leaf and identify the disease that inflicted the crop. Leaf curl disease can be identified by analyzing the edges of the leaf. This paper presents a procedure to identify the curl disease occurring in plant leaves using active contour, Fourier feature descriptor, and deep learning. Active contour is used to identify the shape of the leaf. The edge contour of the leaf is then given to the Fourier feature descriptor. The feature extracted using the Fourier descriptor is invariant to the angle and size of the leaf. The same feature vector is produced in any given angle and size of the leaf in the image. The features are trained using 1D CNN. The model can then be used to classify new images and automatically identify the leaf have curl disease or not. The experimental results prove that the proposed algorithm produces good results in identifying the leaf curl disease.

This article reveals the features of developing a marketing strategy for an enterprise. The development of a marketing strategy is a complex process that requires in-depth research of the state and development of the market, as well as an assessment of the position of the company that it occupies in the market. At the level of the enterprise as a whole, a general strategy is formed that reflects the general strategic line of development and a combination of its possible directions, taking into account the existing market conditions and the capabilities of the company. Strategy focuses on what the firm does and does not do, which is more important and less important in the current activities of the firm. Whatever strategies a company is pursuing, it must be able to react quickly and realign its strategic focus.

This article reveals the features of developing a marketing strategy for an enterprise. The development of a marketing strategy is a complex process that requires in-depth research of the state and development of the market, as well as an assessment of the position of the company that it occupies in the market. At the level of the enterprise as a whole, a general strategy is formed that reflects the general strategic line of development and a combination of its possible directions, taking into account the existing market conditions and the capabilities of the company. Strategy focuses on what the firm does and does not do, which is more important and less important in the current activities of the firm. Whatever strategies a company is pursuing, it must be able to react quickly and realign its strategic focus

Blindness is a growing problem worldwide. The major causes of blindness are glaucoma and diabetic retinopathy. Increased intraocular pressure causes glaucoma. In glaucoma detection, it is very difficult to identify the edge of the optic cup because the image is blurred where blood vessels pass through the optic cup. Current methods do not effectively address the issue of peripheral blurring of the blood vessels surrounding the optic cup. In this paper, it is recommended to automatically detect glaucoma in retinal images using an efficient method. Initially, optic disk and cup segmentation is done by the Convolution Neural Network (CNN) and Modified Region Growing Mechanism (MRG). Then, texture features are extracted from the separated results. Finally, a neural network is used to diagnose glaucoma. The experimental results demonstrate that the proposed approach achieves better glaucoma detection result (accuracy, sensitivity and specificity) compared to few other approaches.

With increase in population the need for food is on rise, in such circumstances, plant diseases prove to be a major threat to agricultural produce and result in disastrous consequences for farmers. Early detection of plant disease can help in ensuring food security and controlling financial losses. The images of diseased plants can be used to identify the diseases. Classification abilities of Convolutional Neural Networks are used to obtain reliable output. Google’s pretrained model ‘Inception v3’ is used. The Inception v3 model is trained over a dataset of diseased plants obtained from ‘Plant Village Dataset’. The developed detection approach is evaluated on measures of F1 score, precision and recall.

On the basis of international marketing principles, the sewing and knitwear market is segmented using cluster method.

The growth of irregular cells within the brain region with the prearrangement of tissues is characterized as a Brain Tumor that leads death to people. Comparing to other categories of cancers, a brain tumor is the most deadly disease that has to be detected and treated in the previous stage. Due to cells' complex formation, the tumor detection process is complicated with simple image processing methodologies. Moreover, for providing proper and efficient treatment to the patients, an exact cancer segmentation and classification technique must process the input of brain images as Magnetic Resonance Imaging (MRI) scans. Based on that, this paper develops a novel approach called Soft Computing based Brain Tumor Detection and Classification (SC-BTDC) with the obtained MRI. In the present scenario of tumor detection from image processing, soft computing techniques play a significant role. Hence, it is adopted in this work. The method contains phases such as pre-processing, Fuzzy c-Means clustering-based segmentation, feature extraction, and image classification. The median pre-processing filter and edge detection methods are incorporated for noise removal and clearly define the image in the stage of the median pre-processing filter. Further, FCM based clustering is performed for the image segmentation process, following, factors of Gray Level Co-Occurrence Matrix (GLCM) found feature extraction is established. The final phase includes the classification process using the soft computing technique called Artificial Neural Network (ANN) classification. The proposed system acquires a higher accuracy rate and is compared with various existing algorithms for proving the efficiency and minimum loss of the proposed algorithm.

Brain tumour segmentation is one of the most critical and time-consuming jobs in the field of medical image processing since a human-assisted manual categorization may lead to incorrect prognosis and diagnosis. Furthermore, when there is a big quantity of data to be handled, it is a time-consuming job to say the least. There is a great deal of variation in brain tumours. There is a resemblance in appearance between tumour and normal tissues, which allows for the extraction of tumour areas from normal tissues. Images grow stubborn as time goes on. Using 2D Magnetic Resonance Imaging, we presented a technique for extracting brain tumours from brain scans. The Fuzzy C-Means clustering technique was used to cluster brain images (MRIs), which was then followed by conventional classifiers and other methods. A convolution neural network is a kind of neural network. The experimental research was conducted out on a real-time dataset including tumours of varying sizes, and Locations, forms, and varying picture intensities are all explored. In the conventional classifier section, we used six different traditional classifiers. Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Multilayer Perception (MLP), and Logistic Regression are examples of machine learning algorithms. Regression, Nave Bayes, and Random Forest are all machine learning techniques that have been incorporated in scikit-learn. Following that, we went on to Convolution Neural Network (CNN) is a kind of neural network that is built using Keras and Tensor flow since it produces superior results. Performance as compared to the conventional ones CNN had an accuracy rate of 97.87 percent in our research, which is very impressive. The In this article, the primary objective is to differentiate between normal and aberrant pixels using texture-based and statistical methods. Characteristics that are based on
 

Automatic detection of vehicles data has been widely used in the area of traffic surveillance system where the efficient traffic management along with safety is the main concept. This project depicts the count of the vehicles present at that particular area of traffic using the data provided by the satellite. The satellite captures the image of the particular traffic junction. This satellite image is further processed in order to find the count of the vehicles. The image contains unwanted objects along with the vehicles. For that image, apply thresholding techniques to detect the vehicles and such that unwanted objects whose gray scale values are below the threshold level will be removed. The designed system converts the satellite captured image into gray scale image. This gray scale image is then converted into binary image. It is proposed to develop a unique algorithm for detecting the vehicles using thresholding techniques. If the intensity value is greater than the threshold value, 8-bit of value 255 is assigned else 8-bit of value 0 will be assigned. The edges of the objects present in the binary image will be obtained. Noise will be reduced using filters. The bright areas which are bounded will shows the vehicles present in the image. Boundary formation is useful for detecting the objects in the image. Using Blob detection method, the properties of the objects are depicted and using the Moore Boundary tracking algorithm the boundaries of the objects are detected. Detecting the vehicles and finding the count of the vehicles are the objectives of this project.

Deep learning have earned major popularity in the today world by captured best results in medical analysis field. This research explained the stages of Oral squamous cell carcinoma using the convolution neural network model in deep Learning. Whenever the pathologist examine the photomicrograph image they faced a lot of difficulties to process and finding the stages of oral squamous cell carcinoma into poorly differentiated, medium differentiated and low differentiated. To avoid the difficulties of stages differentiation, the convolution neural network model has been implemented in this research. In the methodology part of Deep learning basically needs large number of data to perform good result so in this work image augmentation was performed to improve the better performance level of deep learning. Finally segmentation has been implemented and the segmented values are given to the convolution neural networks and it gives better accuracy of 85% when compared with all other deep learning techniques

Due to the advancement of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation plays a important role in image processing. Image segmentation refers to partition of an image into different regions that are similar and different in some characteristics like color, intensity or texture. Different algorithms and techniques have been developed for image segmentation. This paper investigates and compiles some of the technologies used for image segmentation. The various segmentation techniques like Edge Detection, Threshold, Region based, Feature Based Clustering and Neural Network Image Segmentation were discussed in this paper

The traditional system for detecting the infection has been the manual process of diagnosing the stained slides under a microscope. This manual process might consume more time for producing the results and the availability of medical experts is not always assured. Considering this as the primary concern we proposed a strategy which limits the human error while recognizing the presence of malarial parasite in the blood sample by using Image Processing. Hence by automating the diagnosis process, results can be acquired relatively quicker and more accuracy can be expected. The technologies and techniques to patently extract the required features and efficiently classify the infected samples are surveyed. This paper presents a survey of various approaches to automate the detection and classification of infected and uninfected cells.

Segmentation of Pancreas with high accuracy in computerized tomography (CT) results is considered to be a basic issue in both medical image processing and computer-aided diagnosis (CAD). Pancreas segmentation is considered as a difficult task due to its uncertainity in location and in analysis of organs, while it takes very minute division of the entire abdominal CT scans. Because of the accelerated development of the CAD system and therefore the serious need for antiseptic treatments, pancreas segmentation with high accuracy of results is demanded. A new approach is used in this paper, for automated pancreas segmentation of CT images using inter-/intra-slice circumstancial instruction with preprocessing, segmentation, feature extraction, classification.