Noninvasive Optical Imaging Techniques in Clinical Application

Kang, Ung Gu; Bae, Soo-Jin

doi:10.1002/9783527803293.ch28

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection of skin cancer in starting days can be treated well and chances of recovery are high. Through novel techniques, this is possible [ 9 ]. The novel approaches of deep learning methods are mostly applied not only for skin cancer but also for other varieties of cancers like breast cancer, brain cancer, lungs cancer, and prostate cancer.…”

Section: Introductionmentioning

confidence: 99%

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning

Ghazal

Hussain

Khan

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.

show abstract

Section: Introductionmentioning

confidence: 99%