Machine Learning Based Approach for Detection of Lung Cancer in DICOM CT Image

Dev, Chethan; Kumar, K. Naveen; Palathil, Arjun; Anjali, T; Panicker, Vinitha J

doi:10.1007/978-981-13-5934-7_15

Cited by 22 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Knowledge of these formats is essential for data extraction from files and further image processing. These formats typically store medical images and other information about the patient, for example, demographic information about the person being photographed, technical details about the image recording system, or the reason for the screening 15 . When digital imaging systems such as X-ray and Computed Tomography first appeared in clinics, the film served as a medium of exchange and storage.…”

Section: Data Storage Methodsmentioning

confidence: 99%

Detection and Classification of Thoracic Diseases in Medical Images Using Artificial Intelligence Techniques: A Systematic Review

Prakash¹,

Ramamurthy²

2022

ECS Trans.

View full text Add to dashboard Cite

Artificial Intelligence is at the leading edge of innovation and is developing very fast. In recent studies, it has played a progressive and vital role in computer-aided diagnosis. The chest is one of the large body parts of human anatomy and contains several vital organs inside the thoracic cavity. Furthermore, chest radiographs are the most commonly ordered and globally used by physicians for diagnosis. An automated, fast, and reliable detection of diseases based on chest radiography can be a critical step in radiology workflow. This study presents the conduction and results of a systematic review investigating Artificial Intelligence techniques to identify thoracic diseases in medical images. The systematic review was performed according to PRISMA guidelines. The research articles published in English were filtered based on defined inclusion and exclusion criteria.

show abstract

Section: Data Storage Methodsmentioning

confidence: 99%

Detection and Classification of Thoracic Diseases in Medical Images Using Artificial Intelligence Techniques: A Systematic Review

Prakash¹,

Ramamurthy²

2022

ECS Trans.

View full text Add to dashboard Cite

show abstract

“…In Computer-Aided Diagnosis (CADx) methods, segmentation of Computed Tomography (CT) images is a significant stage. As a result, numerous research have been suggested, such as Dev et al [37], who advocated utilising the support vector machine (SVM) approach to diagnose lung cancer from DICOM CT-Scans. The visuals that were evaluated were either malignant or non-cancerous.…”

Section: Segmentationmentioning

confidence: 99%

“…The extraction of features from photos was accomplished using this methodology. HOG was chosen above other feature extraction approaches such as Scale-invariant feature transform descriptors (SIFT) because to its simplicity and speed of implementation [37][38][39][40][41][42]. Deep-learning-based methodologies were utilised to identify COVID -19 and normal (healthy) chest X-ray pictures in this [52] Fig.…”

Section: Feature Extractionmentioning

confidence: 99%

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Nira

Kumar

2022

Multimed Tools Appl

View full text Add to dashboard Cite

As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.

show abstract

“…It can explore the effects of thousands of scenarios instead of actual experiments and be used to study events beyond the reach of expanding the boundaries of experimental science". Deep learning can be quite useful in the real world, recognising various types of cancer like skin cancer [1], lung cancer [2] and many more by just passing the necessary images. Also has various applications in agriculture [?…”

Section: Introductionmentioning

confidence: 99%

Physics-informed Neural Networks approach to solve the Blasius function

Greeshma¹,

Nair²,

Nair³

et al. 2023

Preprint

View full text Add to dashboard Cite

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at η = −5.69.

show abstract

Machine Learning Based Approach for Detection of Lung Cancer in DICOM CT Image

Cited by 22 publications

References 5 publications

Detection and Classification of Thoracic Diseases in Medical Images Using Artificial Intelligence Techniques: A Systematic Review

Detection and Classification of Thoracic Diseases in Medical Images Using Artificial Intelligence Techniques: A Systematic Review

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Physics-informed Neural Networks approach to solve the Blasius function

Contact Info

Product

Resources

About