3 research outputs found
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network
An expansion of aberrant brain cells is referred to as a brain tumor. The
brain's architecture is extremely intricate, with several regions controlling
various nervous system processes. Any portion of the brain or skull can develop
a brain tumor, including the brain's protective coating, the base of the skull,
the brainstem, the sinuses, the nasal cavity, and many other places. Over the
past ten years, numerous developments in the field of computer-aided brain
tumor diagnosis have been made. Recently, instance segmentation has attracted a
lot of interest in numerous computer vision applications. It seeks to assign
various IDs to various scene objects, even if they are members of the same
class. Typically, a two-stage pipeline is used to perform instance
segmentation. This study shows brain cancer segmentation using YOLOv5. Yolo
takes dataset as picture format and corresponding text file. You Only Look Once
(YOLO) is a viral and widely used algorithm. YOLO is famous for its object
recognition properties. You Only Look Once (YOLO) is a popular algorithm that
has gone viral. YOLO is well known for its ability to identify objects. YOLO
V2, V3, V4, and V5 are some of the YOLO latest versions that experts have
published in recent years. Early brain tumor detection is one of the most
important jobs that neurologists and radiologists have. However, it can be
difficult and error-prone to manually identify and segment brain tumors from
Magnetic Resonance Imaging (MRI) data. For making an early diagnosis of the
condition, an automated brain tumor detection system is necessary. The model of
the research paper has three classes. They are respectively Meningioma,
Pituitary, Glioma. The results show that, our model achieves competitive
accuracy, in terms of runtime usage of M2 10 core GPU
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review
Jamun leaf diseases pose a significant threat to agricultural productivity,
negatively impacting both yield and quality in the jamun industry. The advent
of machine learning has opened up new avenues for tackling these diseases
effectively. Early detection and diagnosis are essential for successful crop
management. While no automated systems have yet been developed specifically for
jamun leaf disease detection, various automated systems have been implemented
for similar types of disease detection using image processing techniques. This
paper presents a comprehensive review of machine learning methodologies
employed for diagnosing plant leaf diseases through image classification, which
can be adapted for jamun leaf disease detection. It meticulously assesses the
strengths and limitations of various Vision Transformer models, including
Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT,
IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper
reviews models such as Dense Convolutional Network (DenseNet), Residual Neural
Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural
Network (CNN), and Locally Reversible Transformer. These machine-learning
models have been evaluated on various datasets, demonstrating their real-world
applicability. This review not only sheds light on current advancements in the
field but also provides valuable insights for future research directions in
machine learning-based jamun leaf disease detection and classification
A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia
Pneumonia, a respiratory infection brought on by bacteria or viruses, affects
a large number of people, especially in developing and impoverished countries
where high levels of pollution, unclean living conditions, and overcrowding are
frequently observed, along with insufficient medical infrastructure. Pleural
effusion, a condition in which fluids fill the lung and complicate breathing,
is brought on by pneumonia. Early detection of pneumonia is essential for
ensuring curative care and boosting survival rates. The approach most usually
used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is
to develop a method for the automatic diagnosis of bacterial and viral
pneumonia in digital x-ray pictures. This article first presents the authors'
technique, and then gives a comprehensive report on recent developments in the
field of reliable diagnosis of pneumonia. In this study, here tuned a
state-of-the-art deep convolutional neural network to classify plant diseases
based on images and tested its performance. Deep learning architecture is
compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152,
Mobilenettv2, and DenseNet with 201 layers are among the architectures tested.
Experiment data consists of two groups, sick and healthy X-ray pictures. To
take appropriate action against plant diseases as soon as possible, rapid
disease identification models are preferred. DenseNet201 has shown no
overfitting or performance degradation in our experiments, and its accuracy
tends to increase as the number of epochs increases. Further, DenseNet201
achieves state-of-the-art performance with a significantly a smaller number of
parameters and within a reasonable computing time. This architecture
outperforms the competition in terms of testing accuracy, scoring 95%. Each
architecture was trained using Keras, using Theano as the backend.Comment: I have to remake the artical. Case there was some accuracy proble