In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of the most dangerous types of skin cancer, and its survival rate is 5%. The development of skin cancer has risen over the last couple of years. Early identification of skin cancer can help reduce the human mortality rate. Dermoscopy is a technology used for the acquisition of skin images. However, the manual inspection process consumes more time and required much cost. The recent development in the area of deep learning showed significant performance for classification tasks. In this research work, a new automated framework is proposed for multiclass skin lesion classification. The proposed framework consists of a series of steps. In the first step, augmentation is performed. For the augmentation process, three operations are performed: rotate 90, right-left flip, and up and down flip. In the second step, deep models are fine-tuned. Two models are opted, such as ResNet-50 and ResNet-101, and updated their layers. In the third step, transfer learning is applied to train both fine-tuned deep models on augmented datasets. In the succeeding stage, features are extracted and performed fusion using a modified serial-based approach. Finally, the fused vector is further enhanced by selecting the best features using the skewness-controlled SVR approach. The final selected features are classified using several machine learning algorithms and selected based on the accuracy value. In the experimental process, the augmented HAM10000 dataset is used and achieved an accuracy of 91.7%. Moreover, the performance of the augmented dataset is better as compared to the original imbalanced dataset. In addition, the proposed method is compared with some recent studies and shows improved performance.
Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one’s eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.
As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning. As all ways to deal with e-learning lose their allure and the level of online courses builds, they move towards more customized versatile learning so as to collaborate with students and achieve better learning results. The schools focus on the examination, mindfulness, and arranging techniques that infuse innovation into the vision and educational program. E-learning issues are a standard examination issue for us all. The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances by investigating upsides and downsides of various methods organization. The literature review methodology is to review the cross sectional impacts of e-learning and Machine learning algorithms from existing literatures from the year 1993 to 2020 and to assess the essentialness of elearning features to optimize the e-learning models with available Machine learning techniques from peerinspected journals, capable destinations, and books. Second, it legitimizes the chances of e-learning structures introduction, and changes demonstrated through AI and Machine Learning algorithms. This examination assists in providing helpful new highlights to analysts, researchers and academicians. It gives an exhaustive structure of existing e-learning frameworks for the most recent innovations identified with learning framework capacities and learning tasks to envision ML research openings in appropriate spaces. The survey paper identifies and demonstrates the important role of different types of e-learning features such as Individual pertinent feature, Course pertinent feature, Context pertinent feature and Technology pertinent feature in framework performance tuning. The performance of Machine Learning algorithms to optimize the features of E-Learning models were reviewed in previous literatures and Support Vector Machine technique was found to be the one of the best to predict the input and output parameters of elearning models and it is found that Fuzzy C Means, Deep Learning algorithms are producing better results for Big Data sets.
The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.
Touch screen interaction system is highly demanding for new innovations from the point of visual sensing, as familiar and easily tracing the virtual key board on the screen, three dimensional gesture communications methods, and RFID sensing etc. In spite of the existence of these types of interaction methods, visually impaired people struggle to have easy access to touch screens. The main goal of the research is to overcome the navigation problems that blind people face while interacting with touch screens. In this examination we centered to build up a Braille sketch; a motion put together information technique with respect to contact screen advanced mobile phones for outwardly debilitated individuals. Utilizing Braille codes to perform motions on contact screen makes the outwardly tested people agreeable in light of the fact that Braille is the reason for correspondence. The streamlining procedure is the demonstration of amplifying or limiting a genuine function by systematic choosing input parameters from an accessible pool of parameters and to figure the estimation of function. Here, we accept variables as hand finger motion facilitate, for example, the coordinate values on x and y axises, swipe limit speed, swipe least separation, pixel rate and speed of X and Y. To build the execution of the system structure, we improve by differing shrouded layer and neuron using Crow Search Algorithm (CSO). The ANN with CSA achieves the Optimal Hidden Layer and Neuron (OHLN) to anticipate the right motion yields. These strategies present an answer that will consequently perceive the hand signals so impeded individual can without much of a stretch speak with ordinary individuals. The proposed model will give high precision with ideal execution measurements contrasted with other existing created demonstrate.
Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy > 97%) and concatenated features (accuracy > 98%).
The incident rate of the Gastrointestinal-Disease (GD) in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image (EI/CI) supported evaluation of the GD is an approved practice. Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity. The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp (GP) with better accuracy. The proposed GP detection system consist; (i) Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy (Fuzzy/Shannon/Kapur) and between-class-variance (Otsu) technique, (ii) Automated (Watershed/Markov-Random-Field) and semi-automated (Chan-Vese/Level-Set/Active-Contour) segmentation of GP fragment, and (iii) Performance evaluation and validation of the proposed scheme. The experimental investigation was performed using four benchmark EI dataset (CVC-ClinicDB, ETIS-Larib, EndoCV2020 and Kvasir). The similarity measures, such as Jaccard, Dice, accuracy, precision, sensitivity and specificity are computed to confirm the clinical significance of the proposed work. The outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.
Glaucoma is a major threatening cause, in which it affects the optical nerve to lead a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits and so on. These kinds of causes leads to Glaucoma easily as well as the affection to such disease leads a heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The eye fluid called aqueous humor is getting blocked inside due to Glaucoma, in normal cases sometimes the fluid comes out from the eye via mesh perspective channel, but this Glaucoma blocks that channel and causes the fluid to getting locked inside and provides the permanent blockage inside. So, that the eyes are getting severe affections such as infection, random blindness in initial stages and so on. The World Health Organization analyzes and reports nearly 80 million people around the globe are affected due to some form of Glaucoma. The major problem with this disease is it is incurable, however, the affection stages can be reduced and maintain the same level of affection as it is for the long period but it is possible only earlier stages of identification. This Glaucoma causes structural affection to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads a harmful damage to one's eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility is shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this paper, a new methodology is introduced to identify the Glaucoma on earlier stages called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of affection by such disease using OCT images. The exact position point out is handled by using Region of Interest (ROI) based optical region selection, in which it is easy to point the Optical Cup (OC) and Optical Disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and shows the practical proofs on resulting section in clear manner.
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