Pancreatic Tumor Detection Using Image Processing

Shah, Jeenal; Surve, Sunil; Turkar, Varsha

doi:10.1016/j.procs.2015.04.221

Cited by 14 publications

(5 citation statements)

References 1 publication

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“…The application of computer vision techniques in pancreatic cancer research has the potential to facilitate the identification and localization of pancreatic tumors and other lesions. Through the automated identification of these regions, medical professionals can concentrate their efforts on the specific areas of concern, thereby enabling enhanced precision in diagnosis and treatment strategizing [44,[70][71][72][73]. These algorithms can also help classify tumors into distinct subtypes or determine their malignancy by extracting pertinent features from medical images, such as texture, shape, or intensity patterns [7,74,75].…”

Section: Computer Visionmentioning

confidence: 99%

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Tripathi,

Tabari,

Mansur

et al. 2024

Diagnostics

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Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

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Section: Computer Visionmentioning

confidence: 99%

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Tripathi,

Tabari,

Mansur

et al. 2024

Diagnostics

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show abstract

“…Using a convolutional network within three kernels, the auto differentiate approach is used to identify cancer. The approach simultaneously accomplished the initial identification of the whole core and enhanced regions in dice likeness and quantity metrics by 0.85, 0.81, and 0.75, respectively [17]. Research conducted on the subject of DL and its role in covid-19 which aimed at diagnosing and detection of coronavirus-19 disease through various radiology modalities such as x-rays and Computed Tomography scans (CT scans).…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Approach towards Healthcare using Identity Access Management and Machine Learning

Sharma¹

2022

IJRASET

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Advances around the field of deep learning and cognitive computing have allowed mankind to look at and solve the problems of the world in a completely new way. Early detection of some deadly diseases helps save millions of lives but 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. The health care industry seems to land blocked by their rigid methodologies and to this date, there is no involvement of the patient and caretaker staff in the digitalization of healthcare. In this project, we will introduce a digital platform for healthcare that aims to reduce the gap between a doctor, patient, and the caretaker staff. This platform is based on data, that can assist surgeons, patients, and care teams throughout the patient journey by automating some of the critical processes that are now done manually, including decision-making, post-surgery planning, tracking, and estimating recovery time, smart disease detection models, and collecting patient feedback. An important aspect of this platform will be the disease prediction models wherein we have prepared three different disease prediction models that will be able to detect if a user has that specific disease or not without the consultation of the Doctor. Here we have talked about the adoption of Deep Learning and Artificial Intelligence in today’s Healthcare scenario and the crucial role of delivering such applications to the user on a single platform.

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“…Due to this, many patients remain under-diagnosed, which does not lead clinicians to surgical planning. One of the earliest symptoms of pancreatic cancer is Jaundice [5,6]. The eyes become yellow discoloration of the skin [5] and whites of the eye as in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…The eye image of the patient with jaundice is used to detect whether the pancreas is affected by cancer or not. The symptoms of pancreatic cancer [8] are yellowish of skin and eye, pain or pain in the abdomen, diabetes, nausea, and vomiting, gall bladder or liver enlargement, and blood clots The research's main objective is to link jaundice and pancreatic cancer since jaundice is easily recognizable through the whites of the eye, i.e., sclera. Hence, this becomes the motive to focus on this research.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

Roy¹,

Mala²

2022

Computer Systems Science and Engineering

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Pancreatic cancer is one of the deadliest cancers, with less than 9% survival rates. Pancreatic Ductal Adeno Carcinoma (PDAC) is common with the general public affecting most people older than 45. Early detection of PDAC is often challenging because cancer symptoms will progress only at later stages (advanced stage). One of the earlier symptoms of PDAC is Jaundice. Patients with diabetes, obesity, and alcohol consumption are also at higher risk of having pancreatic cancer. A decision support system is developed to detect pancreatic cancer at an earlier stage to address this challenge. Features such as Mean Hue, Mean Saturation, Mean Value, and Mean Standard Deviation are computed after color space conversion from RGB to HSV. Fuzzy k-Nearest Neighbor (F-kNN) is designed for classification. The system proposed is trained and tested using features extracted from jaundiced eye images. The proposed system results indicate that this model can predict pancreatic cancer as earlier as possible, helping clinicians make better decisions for surgical planning.

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Pancreatic Tumor Detection Using Image Processing

Cited by 14 publications

References 1 publication

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

A Novel Approach towards Healthcare using Identity Access Management and Machine Learning

Early Detection of Pancreatic Cancer Using Jaundiced Eye Images

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