Artificial itelligence in medicine

Singh, Arun Kumar; Tripathi, Ashish; Singh, Krishna Kant; Choudhary, Pushpa; Vashist, Prem Chand

doi:10.1016/b978-0-12-821229-5.00008-2

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Breast cancer is the most common form of malignancy among women worldwide. Advances in medical science have led to various innovative methods 18 for detecting breast cancer.…”

Section: Scientific Literaturementioning

confidence: 99%

Breast cancer: A comparative review for breast cancer detection using machine learning techniques

Khan,

Singh,

Sultana

et al. 2023

Cell Biochemistry & Function

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Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer. The main goal of this review is to explore various techniques of machine learning algorithms to examine high accuracy and early detection of breast cancer for the safe health of women.

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“…Breast cancer is the most common form of malignancy among women worldwide. Advances in medical science have led to various innovative methods 18 for detecting breast cancer.…”

Section: Scientific Literaturementioning

confidence: 99%

Breast cancer: A comparative review for breast cancer detection using machine learning techniques

Khan,

Singh,

Sultana

et al. 2023

Cell Biochemistry & Function

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“…ConvolutionalNeuralNetwork(CNN)whichisalsoknownasConvNet,isanalgorithmofthedeep neuralnetworkwhichismostlyusedtoanalyzevisualimagerybyextractingessentialfeaturesfrom the input image (Singh AK. et al, 2021).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Analyzing Skin Disease Using XCNN (eXtended Convolutional Neural Network)

Tripathi¹,

Singh²,

Singh³

et al. 2022

International Journal of Software Science and Computational Intelligence

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Skin disease is one of the major concerns for clinicians and researchers. Fungus, germs, allergies, and viruses are the main causes of skin diseases. There has always been unsaid competition between conventional and advanced computing-based techniques, and with these new techniques, cost of treatment is also being reduced drastically. In this paper, a deep learning-based model named eXtended Convolutional Neural Network (XCNN) has been proposed to classify three types of skin diseases (i.e., acne, rosacea, and melanoma). XCNN is easy-to-use, economic, and accurate. It will help clinicians to identify and categorize such diseases at the initial stage through automated screening. The proposed work is designed for multi-classification that takes digital images and applies XCNN to identify the type of disease. The model has been built on the dataset of the various skin disease images. It gives 95.67% accuracy in recognizing the diseases with improved recall, f1-score, and precision values compared to other state-of-the-art models.

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