Machine Learning Based Classification of Lung Cancer Using CT Scan Images

ALI, AQIB; NAEEM, SAMREEN; Anam, Sania; Zubair, M. Mujeeb

doi:10.3390/mol2net-08-13908

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…The pathologist performed one reading without the aid of Paige Prostate (the process is also known as an 'unassisted reading'). The other reading was performed with the assistance of Paige Prostate (also known as assisted reading) [24]. According to the findings, using Paige Prostate enhanced cancer detection on single-slide photos by an average of 7.3%, as compared to the unaided readings performed by pathologists on full-slide images of single biopsies.…”

Section: Ai and Digital Pathologymentioning

confidence: 99%

Current State of Artificial Intelligence (AI) in Oncology: A Review

Ali

NAEEM²,

Anam

et al. 2023

CTO

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Artificial intelligence (AI) has made significant strides in recent years toward resolving a wide range of biological issues, including a number of occurrences connected to cancer. Deep learning is an adaptable sector of AI that enables the automatic extraction of features. It is increasingly being used in various fields of cancer research, both scientific and clinical. In this study, several current applications of AI in oncology, including the situations in which deep learning has effectively addressed intractable disputes, are discussed. This study also discusses the challenges that need to be surmounted before such an application of AI may be implemented more broadly. Similarly, it brings to light resources and data sets that can assist in maximizing the potential of AI. Essential insights in oncology may be generated based on the development of novel methodologies and uses of AI, making it possible for substantial changes to be made to clinical practice.

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Section: Ai and Digital Pathologymentioning

confidence: 99%

Current State of Artificial Intelligence (AI) in Oncology: A Review

Ali

NAEEM²,

Anam

et al. 2023

CTO

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show abstract

“…Atlas can be physically labeled; it is related to the dividing table. Many atlases improve partition precession by registering Images [36]. The physically tagged Atlas was modified to "LABEL PROPAGATION" during the Image inscription to split the marked item precisely.…”

Section: ) Model-based Algorithmsmentioning

confidence: 99%

Review on Medical Image Segmentation and Feature Extraction Approaches

Ahmed,

Ali,

Hashmi

et al. 2023

JISR-C

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In medicine, "imaging" refers to the technique or technology of seeing in or out of a living organism to discern the constituent parts of that body. During the operation, the medical diagnosis is examined, an analysis of the disease is performed, and image data sets that are considered normal and abnormal are created. Two different types of images are used in the medical profession. These are medical images that use visible light and those that use invisible light. A radiologist can only decipher the first category of medical images, but a person of average intelligence can understand the second category of medical images. Both segmentation and feature extraction are required to analyze all of them. The authors of this study limit the scope of their research to tumor detection by mammography or magnetic resonance imaging. While there is a wide range of medical imaging techniques, they focus on these two approaches. The authors of this article address several methods for the medical image preprocessing step, known as segmentation and feature extraction.

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Section: Ai and Digital Pathologymentioning

confidence: 99%

Genetic identification and phylogenetic relationships of fish species at Panjnad , Abbasia and Abu Dahbi canals (Punjab, Pakistan) based on mitochondrial COI sequences

Naz

Chatha²,

DANABAS3³

et al. 2022

CTO

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Fish is an extremely nutritious food that is abundant in important minerals, vitamin D, protein, and omega-3 fatty acids. Now a day, mislabeling of fish products is a major problem in the fish industry that affects the market prices and causes fluctuations. Phylogenetic analysis of nucleotide sequences is a technique that might be used to verify the identity of misidentified fish species. In this study five species (Cirrhinus mrigala, Hypophthalmichthys molitrix, two Actinopterygii sp., and Mastacembelus armatus) were used for phylogenetic analysis using the COI gene was amplified using the PCR, average product size is 658 base pairs. A comparison of obtained sequences was implemented through National Center for Biotechnology Information database (NCBI) and the Basic Local Alignment Search Tool (BLAST). Most resemble sequences were downloaded for comparison with our sequencing results. By using MAGA X the rates of different transitional substitutions are 11.33 and transversional substitutions are 6.83. The estimated Transition/Transversion bias (R) is 0.83. The K2P model was used for the estimation of this substitution pattern. Because most freshwater fish species were in the same order or family, phylogenetic tree analysis indicated that they grouped together. Using the InterPro database, the activity and regulatory pathways of the Cytochrome C Oxidase 1 gene are identified to show their activity on the protein level, especially for some important physiological functions. Each species of fish could be identified based on their speciation and node position and distance from their parent nodes. Based on the closest phylogenetic relations, DNA sequence-based study was developed for the accurate identification of fish species.

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Machine Learning Based Classification of Lung Cancer Using CT Scan Images

Cited by 3 publications

References 6 publications

Current State of Artificial Intelligence (AI) in Oncology: A Review

Current State of Artificial Intelligence (AI) in Oncology: A Review

Review on Medical Image Segmentation and Feature Extraction Approaches

Genetic identification and phylogenetic relationships of fish species at Panjnad , Abbasia and Abu Dahbi canals (Punjab, Pakistan) based on mitochondrial COI sequences

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