5 research outputs found
A Medical Analysis for Colorectal Lymphomas using 3D MRI Images and Deep Residual Boltzmann CNN Mechanism
In this technological world the healthcare is very crucial and difficult to spend time for the wellbeing. The lifestyle disease can transform in to the life threating disease and lead to critical stages. Colorectal lymphomas are the 3rd most malignancy death in the entire world. The estimation of the volume of lymphomas is often used by Magnetic Resonance Imaging during medical diagnosis, particularly in advanced stages. The research study can be classified in multiple stages. In the initial stages, an automated method is used to calculated the volume of the colorectal lymphomas using 3D MRI images. The process begins with feature extraction using Iterative Multilinear Component Analysis and Multiscale Phase level set segmentation based on CNN model. Then, a logical frustum model is utilized for 3D simulation of colon lymphoma for rendering the medical data. The next stages is focused on tackling the matter of segmentation and classification of abnormality and normality of lymph nodes. A semi supervised fuzzy logic algorithm for clustering is used for segmentation, whereas bee herd optimization algorithm with scale down for employed to intensify corresponding classifier rate of detection. Finally, classification is performed using Deep residual Boltzmann CNN. Our proposed methodology gives a better results and diagnosis prediction for lymphomas for an accuracy 97.7%, sensitivity 95.7% and specify as 95.8% which is superior than the traditional approach
ΠΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ ΠΏΡΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠΌ ΡΠ°ΠΊΠ΅: ΠΎΠ±Π·ΠΎΡ
The study objective: the study objective is to examine the use of artificial intelligence (AI) in the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) and discuss the future potential of AI in CRC. Material and Methods. The Web of Science, Scopus, PubMed, Medline, and eLIBRARY databases were used to search for the publications. A study on the application of Artificial Intelligence (AI) to the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) was discovered in more than 100 sources. In the review, data from 83 articles were incorporated. Results. The review article explores the use of artificial intelligence (AI) in medicine, specifically focusing on its applications in colorectal cancer (CRC). It discusses the stages of AI development for CRC, including molecular understanding, image-based diagnosis, drug design, and individualized treatment. The benefits of AI in medical image analysis are highlighted, improving diagnosis accuracy and inspection quality. Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. Ethical and regulatory considerations in integrating AI are mentioned, including patient trust, data security, and liability in AI-assisted surgeries. The review emphasizes the importance of an AI standard system, dataset standardization, and integrating clinical knowledge into AI algorithms. Overall, the article provides an overview of the current research on AI in CRC diagnosis, treatment, and prognosis, discussing its benefits, challenges, and future prospects in improving medical outcomes.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ - ΠΎΡΠ΅Π½ΠΊΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° (ΠΠ) Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅, Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ° (ΠΠ Π ), Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΠΠ Π² Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΠ Π . ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ ΠΏΠΎΠΈΡΠΊ Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ Π² ΠΏΠΎΠΈΡΠΊΠΎΠ²ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Web of Science, Scopus, PubMed, Medline ΠΈ eLIBRARY. ΠΡΠ»ΠΎ ΠΏΡΠΎΡΠΌΠΎΡΡΠ΅Π½ΠΎ Π±ΠΎΠ»Π΅Π΅ 100 ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΏΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΠ Π΄Π»Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ, Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠ Π . Π ΠΎΠ±Π·ΠΎΡ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΈΠ· 83 ΡΡΠ°ΡΠ΅ΠΉ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ, ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π½ΠΎΠΉ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° Π² ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Π΅, ΠΎΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ Π΅Π³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠΌ ΡΠ°ΠΊΠ΅. ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΡΡΠ°ΠΏΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ ΠΏΡΠΈ ΠΠ Π , Π²ΠΊΠ»ΡΡΠ°Ρ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ, Π»ΡΡΠ΅Π²ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΡ Π»Π΅ΠΊΠ°ΡΡΡΠ² ΠΈ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠ΅ Π»Π΅ΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠ΄ΡΠ΅ΡΠΊΠ½ΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΠ Π² Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΠΠ’, ΠΠ Π’ ΠΈ ΠΠΠ’, ΡΡΠΎ ΠΏΠΎΠ²ΡΡΠ°Π΅Ρ ΡΠΎΡΠ½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ. Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΡΠ°ΠΊΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ, ΠΊΠ°ΠΊ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΡ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠΈΡΡΠ΅ΠΌΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΠΎΠ΄ΡΠ΅ΡΠΊΠΈΠ²Π°Π΅ΡΡΡ ΡΠΎΠ»Ρ ΠΠ Π² Π²ΡΠ±ΠΎΡΠ΅ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΈ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ²Π°. Π£ΡΠΈΡΡΠ²Π°ΡΡΡΡ ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠ΅ Π°ΡΠΏΠ΅ΠΊΡΡ ΠΠ, Π²ΠΊΠ»ΡΡΠ°Ρ Π΄ΠΎΠ²Π΅ΡΠΈΠ΅ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΡ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΡ Π² ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΠ. ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΠ Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅, Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ°, ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΈ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π»Π΅ΡΠ΅Π½ΠΈΡ
The segmentation of colorectal MRI images.
One of the key criteria that informs patient management decisions for colorectal cancer is the extent of the shortest distance from the edge of the primary tumour to the edge of the mesorectum, also referred to as circumferential resection margin (CRM). This region is resected during surgery. The CRM is difficult for clinicians to measure accurately, particularly from 2D slice data. We present a method for automatically calculating and visualising the CRM distances in colorectal cancer MR images. We use local phase of the monogenic signal calculated from the MR image intensities to find edge and ridge features within the data. A non-parametric mixture model is then used to describe image intensity values within level set framework in order to segment the mesorectal fascia and the corresponding tumour and lymph nodes, as distinct regions. This segmentation is used to provide an automatic analysis of the shortest distance resection margin, and we show that this is consistent with that of the clinically accepted MERCURY method. We use the segmentation to provide a 3D visualisation of where the resection margin is smallest. Finally, we reconstruct a 3D map of the segmented anatomy. Both the visualisation methods provide a useful tool to aid surgeons in their treatment planning