European Journal of Molecular & Clinical Medicine

Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consumes huge percentage of budget. Mostly the interpretations of medical data are being done by medical expert. In terms of image interpretation by human expert, it is quite limited due to its subjectivity, complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in other real-world application, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in health sector. we discussed state of the art deep learning architecture and its optimization used for medical image segmentation and classification. In the last section, we have discussed the challenges deep learning based methods for medical imaging and open research issue.

Background:Reading radiographic images for Covid-19 identification by an expert radiologist requires significant time, therefore the development of an automated analysis system to assisting and saving time in diagnosing Covid-19 is important.
Objective: The purpose of this study is to implement the GoogleNet architecture with various epochs in hope achieving higher level of accuracy in Covid-19 detection.
Methods: We retrospectively used 813 images, i.e. 409 images indicating Covid-19 and 404 normal images. The deep TL model with GoogleNet architecture was implemented.The training was carried out several times to get the best acquisition value with a learning rate of 0.0001 for all levels. The network training was carried out with different epochs, i.e. 12, 18, and 24 epochs, and each epoch with 65 iterations.
Results: It was found that accuracy was determined by changes in the number of epochs. The classification accuracy was 96.9% in epoch 12, 98.2% in epoch 18, and 99.4% in epoch 24.
Conclusion: An increase in the number epochs increases the accuracy in the detection of Covid-19. In this study, the accuracy of the method reached 99.4%. These results are promising for the automation of Covid-19 detection from radiographic images.

Crops suffering from various diseases can be a big turndown for crop yield. This can affect effective crop production, if left unnoticed. Hence, it is extremely important to examine the plant diseases in its initial stages so that felicitous actions can be taken by the farmers at the nick of time, to avoid further losses. It focuses on the method which is based on image processing way for identification of diseases of leaf in a plant .so let’s introduce a system which uses convolutional neural networks that helps farmers to identify any possible plant disease by loading a leaf image in to the system. The system consists of a collection of algorithms which identifies the type of disease with which the leaf is affected by a disease. Input image given by the user goes through many pre-processing steps to identify the disease and results are returned back to the user on a user interface.

This research paper proposes the past decenary, substantial improvement in computational ability and betterment in algorithms for analysis of Images has gained vast fame in resolving challenges in the area of medical diagnosis. Subsequently, computerized tissue histopathology at present is becoming tractable towards the implementation of digitized analysis of images and deep learning methods. Cancer is a cluster of disorders involving irregular cell maturation with the capability to conquer or proliferate to other organs of the body. Detection of cancer in the earlier stages is a exacting task due to which many people are prone to death. Treatment of cancer benefits from the pace, perfection of Deep Learning-obliged practice of diagnosis. Deep Learning techniques are utilized to diagnose the features of progressed carcinoma with enhanced perfection compared to individual pathologist. This paper suggests a deep convolution neural network for categorizing a tissue as malicious, there after segregate the tissue then ultimately perform multi-class detection and classification of Breast Cancer disease and its stages in histopathology images

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.

Glaucoma has arisen as the one of the main sources of visual impairment. A typical technique for diagnosing glaucoma is through assessment optic nerve head by an experienced ophthalmologist. This methodology is arduous and burns-through a lot of time. Despite the fact that the analysis of this infection has not yet been discovered, the period of primary identification can preserve from the glaucoma. Subsequently, customary glaucoma screening is basic and suggested. The issue can be settled by applying machine learning techniques for glaucoma detection. We present an automated glaucoma screening framework using a pre-trained Alexnet model with SVM classifier to enhance the classification accuracy . In this study, we used three publicly available dataset as HRF, Origa and Drishti_GS1 dataset. The proposed model achieved the image classification accuracy of 91.21%. This study showed that using pre-trained CNN with SVM for glaucoma detection showed greater accuracy in automatic image classification than just CNN or SVM.

The death caused by Leukemia has been ranked in the top ten most dangerous mortality cause for the human being. There are numerous reasons and causes, in spite of the causes and reasons the profound problem is the slow decision-making process which delays the time required to proceed with medical treatment for the patients. That’s why the enhanced medical support process has become necessary for the classification of leukemia. The four different types of Leukemia are as follows Myeloid Leukemia where we have acute and chronic subcategories and in the same way, it goes for the myeloid type as well, these affect various cells and systems such as the blood cells, bone marrow, lymphatic system and which causes the death of patients. The proposed method improves the CML, CLL, AML and ALL characteristic accuracy by scanning color and textural features from the blood image using image processing and to aid in the grouping of CML, CLL, AML and ALL. The following technique proposes a quantitative microscopic approach toward the grouping of blood sample images. A model using Modified Convolution Neural Network (CNN) architecture is used to optimize the classification process. Based on optimized feature space, a CNN model with various kernel functions (filters) used to abstract the features from the pixel values. The proposed method is tested using nearly 10000 microscopic blood images. The outcome confirmed that the accuracy of the classification using blood sampled images which was up to 98%.