An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm

Vidhya, R.G.; Seetha, J.; Ramadass, Sudhir; Dilipkumar, S.; Sundaram, Ajith; Saritha, G.

doi:10.1109/iccpc55978.2022.10072277

Cited by 17 publications

(3 citation statements)

References 13 publications

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“…Alzheimer's disease, Parkinson's disease, and other forms of neurodegeneration can all benefit greatly from the predictive insights that can be gleaned from multi-modal data using machine learning (Seetha et al 2022). Machine learning models can extract useful information and provide predictions regarding illness development, early diagnosis, and treatment response by merging data from many modalities such clinical assessments, neuroimaging, genetic markers, and cognitive tests.…”

Section: Machine Learning On Multi Model Date On Providing Insight To...mentioning

confidence: 99%

β-amyloid deposition-based research on neurodegenerative disease and their relationship in elucidate the clear molecular mechanism

Wange,

Khan,

Pinnamaneni

et al. 2023

Multidiscip. Sci. J.

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Alzheimer's disease (AD) is characterized by the central feature of dysregulated metabolism of β-amyloid peptide (Aβ). BACE1, which is the enzyme responsible for the production of Aβ, has been identified as a promising therapeutic target. Recent research has uncovered a regulatory correlation between BACE1 and Clusterin, a glycoprotein that plays a role in the clearance of Aβ. Research has demonstrated that the inhibition or downregulation of BACE1 results in elevated expression of Clusterin in astrocytes, which subsequently promotes the effective removal of Aβ. Clusterin functions as a molecular chaperone, facilitating the internalization and degradation of amyloid-beta (Aβ) by astrocytes. The aforementioned regulatory pathway exhibits a compelling avenue for therapeutic interventions in Alzheimer's disease. The modulation of BACE1 activity with the aim of promoting Clusterin expression and subsequent clearance of Aβ has the potential to mitigate the burden of Aβ and ameliorate the pathology associated with Alzheimer's disease. Additional investigation is required to clarify the fundamental molecular mechanisms and assess the therapeutic viability of directing attention towards BACE1 and Clusterin in relation to Aβ elimination. Comprehending this regulatory pathway has the potential to provide novel perspectives on the pathogenesis of Alzheimer's disease and direct the advancement of inventive therapeutic approaches.

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Section: Machine Learning On Multi Model Date On Providing Insight To...mentioning

confidence: 99%

β-amyloid deposition-based research on neurodegenerative disease and their relationship in elucidate the clear molecular mechanism

Wange,

Khan,

Pinnamaneni

et al. 2023

Multidiscip. Sci. J.

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“…Mammography, an imaging technique that uses a low dosage of X-rays, has been the gold standard for screening women for breast cancer for a good number of years. [7] It has been established that the mortality rates associated with breast cancer are going down thanks to this method of early tumor diagnosis. A mammography is able to detect any abnormalities in the breast tissue, such as calcifications or small lumps, that may be malignant.…”

Section: Motivation and Previous Workmentioning

confidence: 99%

Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

Reddy,

M L,

Praveena

et al. 2023

IJRITCC

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Cancer of the breast is a malignant tumour that originates in the cells of the breast tissue. It is by far the most common kind of cancer found in females around the world, with a projected 2.3 million new cases will be discovered in the year 2020 alone. It is projected that one in eight women will be diagnosed with breast cancer at some point in their life, despite the fact that breast cancer can also occur in men. Breast cancer is a complex condition that can arise from a diverse set of factors, express itself in a variety of ways, and can be treated in a variety of ways. Ductal carcinoma in situ, invasive ductal carcinoma, and invasive lobular carcinoma are all different subtypes. Both the available treatment options and the expected outcome of breast cancer are very variable depending on the particular subtype of the illness. Breast cancer risk factors include drinking alcohol and not getting enough exercise, as well as getting older, having a family history of the disease, having genetic mutations, being exposed to estrogens, and having a family history of the disease. There is not always a connection between having risk factors and developing breast cancer, despite the fact that there can be a link between the two. The prognosis and treatment options for breast cancer are highly dependent on the stage of the disease at the time of diagnosis. During staging, the extent to which the cancer has spread throughout the body and how far it has progressed are both measured. The TNM system, the IAFCM system, the ACM system, and the MPIG system are just few of the staging systems that are used to classify breast cancer. These staging systems consider not only the size of the tumor but also whether or not lymph nodes are involved and whether or not distant metastases are present. The severity of breast cancer symptoms can vary widely, depending not only on the subtype of the disease but also on how far along it has progressed. Alterations in the size or shape of the breast, discharge from the nipple, and alterations in the skin of the breast (such as redness or dimpling) are all common indications. On the other hand, not all cases of breast cancer present themselves in a visible manner, and mammography and other forms of routine screening may be able to detect some of these cases. Options for treating breast cancer vary depending on the patient's condition and the stage of the disease, as well as the patient's overall health and their preferences towards therapy. Common examples of medical interventions include surgery, radiotherapy, chemotherapy, hormone therapy, and targeted therapy. Other examples include. In certain cases, it may be appropriate to participate in more than one form of treatment.

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“…Genomic profiling and the molecular characterization of cancers are bringing new insights into possible treatment targets and biomarkers that could be used to stratify patients. The combination of artificial intelligence (AI) and machine learning algorithms makes it possible to conduct automated analyses of imaging data, which helps with the segmentation of tumours, the evaluation of patient responses, and the formulation of treatment plans [12]. In addition, efforts in the field of research are concentrated on gaining a better knowledge of the relationships between cancer microenvironment, immune regulation, and the creation of personalized therapeutic options.…”

Section: Literature Review 21 Introductionmentioning

confidence: 99%

Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method

Balasubramani Ramesh,

Sudhagar Dhandapani,

Sanda Sri Harsha

et al. 2023

ARASET

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Brain tumour identification and categorization are critical for diagnosis and treatment. This work uses preprocessing and classification algorithms to discover and categories brain tumours. Gaussian smoothing reduces noise and improves image quality, Genetic Algorithms select and optimize features, Deep Learning-Based Segmentation accurately segments tumours, Local Binary Patterns (LBP) extract texture features, and K-Nearest Neighbors classify tumours. Gaussian smoothing reduces noise and improves brain imaging data. Genetic Algorithms extract the most relevant and discriminative features from preprocessed photos. A Deep Learning-Based Segmentation model accurately segments brain tumour regions using these features. After segmentation, Local Binary Patterns (LBP) extract tumour texture features. The K-Nearest Neighbors (KNN) method classifies tumours using these texture features to capture tumour spatial patterns. Our suggested brain tumour detection and classification method combines several techniques to increase accuracy and reliability. Gaussian smoothing and LBP feature extraction improve feature discrimination. Deep Learning-Based Segmentation and the KNN classifier ensure precise tumour location and robust classification. The proposed method will be tested on brain scans including tumour areas. Classification performance measures include accuracy, sensitivity, specificity, and AUC. This work will improve brain tumour detection and classification methods for more accurate diagnoses and treatment planning. The primary goal of the work is to enhance brain tumour identification and categorization using pre processing, classification algorithms, and advanced techniques, ultimately improving diagnosis and treatment outcomes.

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An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm

Cited by 17 publications

References 13 publications

β-amyloid deposition-based research on neurodegenerative disease and their relationship in elucidate the clear molecular mechanism

β-amyloid deposition-based research on neurodegenerative disease and their relationship in elucidate the clear molecular mechanism

Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method

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