Performance analysis of hybrid deep learning framework using a vision transformer and convolutional neural network for handwritten digit recognition

Digitization created a demand for highly efficient handwritten document recognition systems. A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an essential element of handwritten documents. Accurate recognition of handwritten digits is vital for effective communication and data analysis. Various researchers have attempted to address this issue with modern convolutional neural network (CNN) techniques. Even after training, CNN filter weights remain unchanged despite the high identification accuracy. As a result, the process cannot flexibly adapt to input changes. Hence computer vision researchers have recently become interested in Vision Transformers (ViTs) and Multilayer Perceptrons (MLPs). The shortcomings of CNNs gave rise to a hybrid model revolution that combines the best elements of the two fields. This paper analyzes how the hybrid convolutional ViT model affects the ability to recognize handwritten digits. Also, the real-time data contains noise, distortions, and varying writing styles. Hence, cleaned and uncleaned handwritten digit images are used for evaluation in this paper. The accuracy of the proposed method is compared with the state-of-the-art techniques, and the result shows that the proposed model achieves the highest recognition accuracy. Also, the probable solutions for recognizing other aspects of handwritten documents are discussed in this paper.•Analyzed the effect of convolutional vision transformer on cleaned and real-time handwritten digit images.•The model\u27s performance improved with the implication of cross-validation and hyper-parameter tuning.•The results show that the proposed model is robust, feasible, and effective on cleaned and uncleaned handwritten digits

Bibliometric Review on Image Based Plant Phenotyping

Plant phenotyping is a quantitative description of structural, physiological and temporal traits of plants resulting from interaction of plant genotypes with the environment. A rapid development is in progress in the field of image-based plant phenotyping. Plant phenotyping has wide range of applications in plant breeding research, plant growth prediction, biotic and abiotic stress analysis, crop management and early disease detection. The main motive is to provide detailed bibliometric review in order to know the available literature and current research trends in the area of plant phenotyping using plant images. The bibliometric analysis is primarily based on Scopus, web of science, Research Gate and Mendeley. This bibliometric review covers various topics related to image-based plant phenotyping starting from different imaging techniques used for phenotyping to various phenotyping methodologies like image processing, computer vision, machine learning, and deep learning-based plant phenotyping. There is significant advancement observed in the area of plant phenotyping since 2015. It is also observed that researchers from United States are leading the research in plant phenotyping

Bibliometric Review on Liver and Tumour Segmentation using Deep Learning

One of the major organs in the body is liver where tumors occur often. Malignant liver tumors pose a serious hazard to human life and health. Manual segmentation of the liver organ and tumor from computed tomography (CT) scans is difficult, time-consuming, and skewed to the clinician\u27s experience, yet it is essential for hepatic surgical planning. However, due to the following considerations, segmenting liver tumors from computed tomography (CT) images is difficult: In CT pictures, the contrast between the liver tumor and healthy tissues is low, and the boundary is indistinct; the picture of the liver tumor is confusing, with a wide range of size, shape, and location. Since there have been a lot of medical imaging techniques with their own advantages and disadvantages over the years, such as MRI, Ultra-sonography (US), Computed Tomography (CT), so on and so forth, CT is often preferred due to its high sensibility (93 %) and specificity (93 %), where CT is often preferred due to its high specificity (93 %) (100 %)

Bibliometric Review on Applications of Disease Detection using Digital Image Processing Techniques

Advances around the field of deep learning and cognitive computing have allowed mankind to look and solve the problems of the world in a completely new way. Deep learning has been making huge advancements in the field of healthcare, which most importantly focuses upon disease detection and disease prediction. Techniques such as these have been conceptualized the idea of early detection and economical ways of treating the predicted disease in particular. Still, it has been observed that there seems to be no change in the way diagnosis of a particular disease takes place even in the 21st generation of medical health care. The highlight of the reasons happens to be lack of trust, lack of awareness and lack of infrastructure. In this paper we will discuss three disease prediction models and the impact their adoption will provide to millions of lives. The diseases are brain tumour, pancreatic cancer and covid-19. This paper focuses upon the impact of how the adoption of deep learning and artificial intelligence will have a huge positive impact and conceptualise a new way of medical imaging. here we have talked about the adoption of deep learning in models in today’s healthcare scenario and also the crucial role of delivering such applications to the user

Exploration of advancements in handwritten document recognition techniques

Handwritten document recognition and classification are among the many computers related issues being studied for digitizing handwritten data. A handwritten document comprises text, diagrams, mathematical expressions, numerals, and tables. Due to the variety of writing styles and the intricacy of the written language, it has proven difficult to recognize handwritten material. As a result, numerous handwritten document recognition systems have been developed, each with unique benefits and drawbacks. The paper reviews the evolution of handwritten document recognition in qualitative and quantitative ways. Initially, the bibliometric survey is presented based on the number of articles, citations, countries, authors, etc., on handwritten document recognition in the Scopus database. Later, a survey is done on the learning techniques used for handwritten documents: text recognition, digit recognition, mathematical expression recognition, table recognition, and diagram recognition. This paper also presents the directions for future research in handwritten document recognition

Radiomics for Parkinson's disease classification using advanced texture-based biomarkers

Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP. • Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder. • Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis. • The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection

Automatic Segmentation of Pancreas and Pancreatic Tumor: A Review of a Decade of Research

In the current era of machine learning and radiomics, one of the challenges is the automatic segmentation of organs and tumors. Tumor detection is mostly based on a radiologist’s manual reading, which necessitates a high level of professional abilities and clinical experience. Moreover, increasing the high volume of images makes radiologists’ assessments more challenging. Artificial intelligence (AI) can assist clinicians in diagnosing cancer at an early stage by providing a solution for assisted medical image analysis. The automated segmentation of tumor is better realized through conventional segmentation methods and, nowadays, through machine learning and deep learning techniques. The segmentation of abdominal organs and tumors from various imaging modalities has gained much attention in recent years. Among these, pancreas and pancreatic tumor are the most challenging to segment and have recently drawn a lot of attraction. The main objective of this paper is to give a summary of different automated approaches for the segmentation of pancreas and pancreatic tumors and to perform a comparative analysis using various indices such as dice similarity coefficient (DSC), sensitivity (SI), specificity (SP), precision (Pr), recall and Jaccard index (JI), etc. Finally, the limitations and future research perspectives of pancreas and tumor segmentation are summarized