Integrating a novel SRCRN network for segmentation with representative batch-mode experiments for detecting melanoma

Bektaş, Jale; Bektaş, Yasin; Kangal, E. E.

doi:10.1016/j.bspc.2021.103218

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Computer-based deep convolutional neural networks (CNNs) (Bektaş, 2022) have achieved significant success in the processes such as object detection and segmentation, as well as imaging and evaluation of objects with a computer, making them one of the indispensable methods in the detection of difficult diseases such as cancer. For this reason, more attention has been given to the development of computer-based evaluation algorithms for medical imaging in recent decades and there are increasingly studied different CNN architectures in this direction (Yoo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Prostate Cancer via Machine Learning

SÖĞÜT,

KANGAL

2023

International Journal of Pure and Applied Sciences

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By training computers with machine learning technique, patients can be prevented from being exposed to unnecessarily difficult examinations. In recent years, machine learning-based disease assessment approach has gained importance in terms of the benefits it provides to clinical methods. There is a remarkable increase in studies in this direction. There are a limited number of clinical guiding parameters in predicting some types of cancer, and this limitation pushes the patients under treatment to a very frustrating process. For this reason, apart from ordinary procedure of the traditional medicine, an alternative approach to predict the any type of cancer is making a computer-based evaluation that has become a highly studied method in recent years. In this study, a machine learning (ML) approach will be used to evaluate prostate cancer, which is the second most common cancer-related death in men worldwide. For this purpose, the K-Nearest Neighbor (kNN) algorithm based on ML will be used with feature selection, which is a dimension reduction technique. An open source database, Kaggle, was used for the evaluation. The accuracy value of the used algorithm was found 88%.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Prostate Cancer via Machine Learning

SÖĞÜT,

KANGAL

2023

International Journal of Pure and Applied Sciences

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Transfer Learning-Based Encoder-Decoder Model for Skin Lesion Segmentation

Appati,

Boante,

Owusu

et al. 2023

Communications in Computer and Information Science

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A Technological Perspective on Traditional Medicine: Classification of Plant Species with Machine Learning

SÖĞÜT,

REŞİTOĞLU,

KANGAL

2023

Mersin Üniversitesi Tıp Fakültesi Lokman Hekim Tıp Tarihi Ve Folklorik Tıp Dergisi

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The use of medicinal plants with rich chemical content has been one of the methods used for centuries to solve the basic health problems for both developed and underdeveloped societies. It is known that this method is used effectively in Far East countries. The level of success that modern medicine has reached in protecting and treating human health has led to the oblivion of traditional medicine and therefore of what is known about medicinal plants. It seems that traditional medicine will continue to exist in our lives for a while due to some reasons such as the rapid increase in the world population, the inability of every society to reach the drugs at the desired level and the sometimes negative effects of drugs. For this reason, diagnosing and correctly identifying plants with medicinal contents is a very vital situation for people who will use these plants for therapeutic purposes. Knowing the ingredients of any herb is the work of a specialist. An ordinary person's misidentification and use of a plant for therapeutic purposes can cause negative consequences. For this reason, the aim of this study is to identify any plant with the help of its visual morphological features such as the leaf shape, color or odor and to make an accurate description of its medicinal content by image processing and machine learning method. For this, medicinal plants could be identified with 90% accuracy by using the KAGGLE database, which contains 100 images of leaves and flowers for each of the two plants.

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Integrating a novel SRCRN network for segmentation with representative batch-mode experiments for detecting melanoma

Cited by 4 publications

References 29 publications

Evaluation of Prostate Cancer via Machine Learning

Evaluation of Prostate Cancer via Machine Learning

Transfer Learning-Based Encoder-Decoder Model for Skin Lesion Segmentation

A Technological Perspective on Traditional Medicine: Classification of Plant Species with Machine Learning

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