Hybrid data mining technique based breast cancer prediction

Padmanaban, K.; Kumar, Anshuman; Azath, H.; Velmurugan, A. K.; Subbiah, Murugan

doi:10.1063/5.0110216

Cited by 29 publications

(5 citation statements)

References 4 publications

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“…The findings discussed in this section are achieved by applying the proposed inception architecture to the dermoscopic images in the PH2 database [24], [25]. Researchers use the PH2 database more often than any other database that is currently accessible.…”

Section: Resultsmentioning

confidence: 99%

Design of inception architecture for skin melanoma classification

Manikandan

Karthikeyan

2023

IJEECS

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The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods of examination may be used to investigate it. However, the invasive method is more difficult for the patient because samples must be taken from the lesion itself, or the whole lesion must be cut out. It also requires more time and cost. To avoid invasive procedures, computer-based analysis and diagnosis have the potential to increase diagnostic accuracy and turnaround time. This study develops a unique discriminative deep learning architecture (DDLA) for dermoscopic image classification (DIC), called DDLA-DIC, which uses the concept of inception. Using this concept, the proposed DDLA-DIC system is designed wider and deeper and the network learns from various spatial patterns. The proposed DDLA-DIC system can extract image characteristics from dermoscopic images for skin cancer diagnosis in an effective and efficient way. The proposed DDLA-DIC system is evaluated by utilizing the dermoscopic images from the PH2 database, and the obtained classification results are based on a random split approach. The simulation results indicate that the framework has a great deal of potential with 99.79% accuracy.

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Section: Resultsmentioning

confidence: 99%

Design of inception architecture for skin melanoma classification

Manikandan

Karthikeyan

2023

IJEECS

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“…Early detection and diagnosis of breast cancer is important to improve survival rates significantly. ML 22 and data mining 1 techniques are being applied in medical diagnostics, and these methods have already saved lives by accurate prediction of longterm conditions like cancer. Cancer prediction is one example of how ML and deep learning (DL) are used to better our healthcare system.…”

Section: Scientific Literaturementioning

confidence: 99%

“…Breast cancer is the most frequent form of cancer among females worldwide. Although it was India's fourth most frequent cancer 1 in 2020. Due to the recent explosion in medical research funding, early disease identification has become an urgent and utmost need.…”

Section: Introductionmentioning

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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“…Last few years, enormous research has been going on for image restoration and enhancement. Image restoration is dealt with in an objective manner and it is related to feature extraction from the imperfect image and the function of image enhancement is kind of subjective [3]. It could not be precisely represented by mathematical function and also it is manipulated the degraded image, improves the contrast of the image and visual appearance could be improved.…”

Section: Related Workmentioning

confidence: 99%

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

Malathi,

Manikandan

2023

IJECE

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Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image.

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Hybrid data mining technique based breast cancer prediction

Cited by 29 publications

References 4 publications

Design of inception architecture for skin melanoma classification

Design of inception architecture for skin melanoma classification

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

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

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