Comparing primate’s ventral visual stream and the state-of-the-art deep convolutional neural networks for core object recognition

Our ability to recognize and categorize objects in our surroundings is a critical component of our cognitive processes. Despite the enormous variations in each object's appearance (Due to variations in object position, pose, scale, illumination, and the presence of visual clutter), primates are thought to be able to quickly and easily distinguish objects from among tens of thousands of possibilities. The primate's ventral visual stream is believed to support this view-invariant visual object recognition ability by untangling object identity manifolds. Convolutional Neural Networks (CNNs), inspired by the primate's visual system, have also shown remarkable performance in object recognition tasks. This review aims to explore and compare the mechanisms of object recognition in the primate's ventral visual stream and state-of-the-art deep CNNs. The research questions address the extent to which CNNs have approached human-level object recognition and how their performance compares to the primate ventral visual stream. The objectives include providing an overview of the literature on the ventral visual stream and CNNs, comparing their mechanisms, and identifying strengths and limitations for core object recognition. The review is structured to present the ventral visual stream's structure, visual representations, and the process of untangling object manifolds. It also covers the architecture of CNNs. The review also compared the two visual systems and the results showed that deep CNNs have shown remarkable performance and capability in certain aspects of object recognition, but there are still limitations in replicating the complexities of the primate visual system. Further research is needed to bridge the gap between computational models and the intricate neural mechanisms underlying human object recognition.Our ability to recognize and categorize objects in our surroundings is a critical component of our cognitive processes. Despite the enormous variations in each object's appearance (Due to variations in object position, pose, scale, illumination, and the presence of visual clutter), primates are thought to be able to quickly and easily distinguish objects from among tens of thousands of possibilities. The primate's ventral visual stream is believed to support this view-invariant visual object recognition ability by untangling object identity manifolds. Convolutional Neural Networks (CNNs), inspired by the primate's visual system, have also shown remarkable performance in object recognition tasks. This review aims to explore and compare the mechanisms of object recognition in the primate's ventral visual stream and state-of-the-art deep CNNs. The research questions address the extent to which CNNs have approached human-level object recognition and how their performance compares to the primate ventral visual stream. The objectives include providing an overview of the literature on the ventral visual stream and CNNs, comparing their mechanisms, and identifying strengths and limitations for core object recognition. The review is structured to present the ventral visual stream's structure, visual representations, and the process of untangling object manifolds. It also covers the architecture of CNNs. The review also compared the two visual systems and the results showed that deep CNNs have shown remarkable performance and capability in certain aspects of object recognition, but there are still limitations in replicating the complexities of the primate visual system. Further research is needed to bridge the gap between computational models and the intricate neural mechanisms underlying human object recognition

Three Step Authentication of Brain Tumour Segmentation Using Hybrid Active Contour Model and Discrete Wavelet Transform

An innovative imaging research is expected in the medical field due to the challenges and inaccuracies in diagnosing the life-threatened harmful tumours. Brain tumor diagnosis is one of the most difficult areas of study in diagnostic imaging, with the maximum fine for a small glitch given the patients survival rate. Conventionally, biopsy method is used to identify the tumour tissues from the brain's soft tissues by the medical researchers (or) practitioners and it is unproductive due to: (i) it requires more time, and (ii) it may have errors. This paper presents the three-stage authentication-based hybrid brain tumour segmentation process and it makes the detection more accrual. Primarily, tumour area is segmented from a magnetic resonance image and after that when comparing a differentiated segment of an image to the actual image, an improved active contour model is employed to achieve a good match. In addition, discrete wavelet transform is used for the features extraction which leads to improve the accuracy and robustness in the tumour diagnosis. Finally, RELM classifier is used for precise classification of brain tumours. The most effective section of our method is checking the status of the tumour through finding the tumour region. The results are evaluated through new dataset, and it demonstrates that the suggested approach is more efficient than the alternatives as well as provides 96.25% accuracy

Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry

Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization

Anemia Detection using a Deep Learning Algorithm by Palm Images

Our aim is to detect anemia through a comparative analysis of three convolutional neural network (CNN) models, namely EfficientNet B3, DenseNet121, and CNN AllNet. A collection of 3,000 microscopic palm pictures, including 1,500 anaemic and 1,500 non-anemic samples, was used to train and test the algorithms. The dataset was preprocessed to balance the classes, augment the images, and normalize the pixel values. The models were trained using transfer learning on the ImageNet dataset and fine-tuned on the anemia dataset. The performance of the models was evaluated based on accuracy, precision, recall, and F1-score. The results showed that CNN ALLNET achieved the highest accuracy of 96.8%, followed by DenseNet121 with 94.4%, and EfficientNet B3 with 91.2%. The precision, recall, and F1-score also followed a similar trend. The study concludes that CNN ALLNET is the optimal model for anemia detection due to its high accuracy and overall better performance when compared with the different models. The findings of this research could provide a basis for further studies on anemia detection using CNN models, ultimately improving the accuracy and efficiency of anemia diagnosis and treatment

Computational Mechanisms of Face Perception

The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for collaboration between the two disciplines demands emphasis. It\u27s both intriguing and challenging to study the brain using computer science approaches, and this dissertation centers on exploring computational mechanisms related to face perception. Face recognition, being the most active artificial intelligence research area, offers a wealth of data resources as well as a mature algorithm framework. From the perspective of neuroscience, face recognition is an important indicator of social cognitive formation and neural development. The ability to recognize faces is one of the most important cognitive functions. We first discuss the problem of how the brain encodes different face identities. By using DNNs to extract features from complex natural face images and project them into the feature space constructed by dimension reduction, we reveal a new face code in the human medial temporal lobe (MTL), where neurons encode visually similar identities. On this basis, we discover a subset of DNN units that are selective for facial identity. These identity-selective units exhibit a general ability to discriminate novel faces. By establishing coding similarities with real primate neurons, our study provides an important approach to understanding primate facial coding. Lastly, we discuss the impact of face learning during the critical period. We identify a critical period during DNN training and systematically discuss the use of facial information by the neural network both inside and outside the critical period. We further provide a computational explanation for the critical period influencing face learning through learning rate changes. In addition, we show an alternative method to partially recover the model outside the critical period by knowledge refinement and attention shifting. Our current research not only highlights the importance of training orientation and visual experience in shaping neural responses to face features and reveals potential mechanisms for face recognition but also provides a practical set of ideas to test hypotheses and reconcile previous findings in neuroscience using computer methods

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology

What It Is To Be Conscious: Exploring the Plasibility of Consciousness in Deep Learning Computers

As artificial intelligence and robotics progress further and faster every day, designing and building a conscious computer appears to be on the horizon. Recent technological advances have allowed engineers and computer scientists to create robots and computer programs that were previously impossible. The development of these highly sophisticated robots and AI programs has thus prompted the age-old question: can a computer be conscious? The answer relies on addressing two key sub-problems. The first is the nature of consciousness: what constitutes a system as conscious, or what properties does consciousness have? Secondly, does the physical make-up of the robot or computer matter? Is there a particular composition of the robot or computer that is necessary for consciousness, or is consciousness unaffected by differences in physical properties? My aim is to explore these issues with respect to deep-learning computer programs. These programs use artificial neural networks and learning algorithms to create highly sophisticated, seemingly intelligent computers that are comparable to, yet fundamentally different from, a human brain. Additionally, I will discuss the required actions we must take in order to come to a consensus on the consciousness of deep learning computers

Deep Learning Technique for Detecting and Analysing Ischemic Stroke Using MRI Images

The quantitative analysis of cerebral MRI images plays a pivotal role in stroke diagnosis and treatment. Deep learning, particularly CNNs, with their robust learning capabilities, offer an effective tool for lesion detection. To address the unique properties of stroke injuries and automate detection processes, we compiled a dataset of brain MRI images from various medical sources, representing patients affected by ischemic strokes. Different deep learning-based networks, including “Single Shot Multibox Detector (SSD)”, “Region-based CNN with ResNet101 (RCNN-ResNet101)”, “RCNN with VGG16 (RCNN- VGG16)”, and “YOLOV3”, were employed for automated lesion detection. The evaluation focused on achieving optimal precision in comparison to existing methods across Diffused Weight, Flair, and T1 modalities of MRI datasets. The developed technique involves extracting deep features during the encoding stage, followed by the minimization of features using fully connected layers. Significant handcrafted features, such as Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM), were incorporated alongside deep features. The concatenation of these features was implemented to maximize the dimension of the feature vector. This concatenated vector was then used to train and test the performance of various classifiers. Binary classification was employed to categorize brain images into normal or stroke affected. Initially, SoftMax was used as the default classifier. The performance of each classifier was individually evaluated, and the best-performing classifier was selected to confirm the overall effectiveness of the proposed technique. This all-encompassing strategy not only leverages deep learning for automatic lesion detection but also integrates handcrafted features and diverse classifiers to improve the precision and dependability of stroke detection across various brain MRI image modalities

Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI

Alzheimer's is a brain disease that gets worse over time and affects memory, thinking, and behavior. Alzheimer's disease (AD) can be treated and managed if it is diagnosed early, which can slow the progression of symptoms and improve quality of life. In this study, we suggested using the Visual Transformer (ViT) and bi-LSTM to process MRI images for diagnosing Alzheimer's disease. We used ViT to extract features from the MRI and then map them to a feature sequence. Then, we used Bi-LSTM sequence modeling to keep the interdependencies between related features. In addition, we evaluated the performance of the proposed model for the binary classification of AD patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Finally, we evaluated our method against other deep learning models in the literature. The proposed method performs well in terms of accuracy, precision, F-score, and recall for the diagnosis of AD