Classification Model for Skin Cancer Diagnosis in Vitro Using Raman Spectroscopy

2010 ◽  
Author(s):  
Benito Bodanese ◽  
Landulfo Silveira ◽  
Regiane Albertini ◽  
Renato A. Za^ngaro ◽  
Marcos T. T. Pacheco ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Cancer Diagnosis ◽  
Classification Model

scholarly journals Android skin cancer detection and classification based on MobileNet v2 model

2020 ◽  
Vol 6 (2) ◽  
pp. 135
Author(s):  
Adi Wibowo ◽  
Cahyo Adhi Hartanto ◽  
Panji Wisnu Wirawan
Keyword(s):  
Image Analysis ◽  
Skin Cancer ◽  
Object Detection ◽  
Cancer Detection ◽  
Cancer Diagnosis ◽  
Learning Algorithm ◽  
Computing Time ◽  
Classification Model ◽  
Method Accuracy ◽  
Skin Cancer Detection

The latest developments in the smartphone-based skin cancer diagnosis application allow simple ways for portable melanoma risk assessment and diagnosis for early skin cancer detection. Due to the trade-off problem (time complexity and error rate) on using a smartphone to run a machine learning algorithm for image analysis, most of the skin cancer diagnosis apps execute the image analysis on the server. In this study, we investigate the performance of skin cancer images detection and classification on android devices using the MobileNet v2 deep learning model. We compare the performance of several aspects; object detection and classification method, computer and android based image analysis, image acquisition method, and setting parameter. Skin cancer actinic Keratosis and Melanoma are used to test the performance of the proposed method. Accuracy, sensitivity, specificity, and running time of the testing methods are used for the measurement. Based on the experiment results, the best parameter for the MobileNet v2 model on android using images from the smartphone camera produces 95% accuracy for object detection and 70% accuracy for classification. The performance of the android app for object detection and classification model was feasible for the skin cancer analysis. Android-based image analysis remains within the threshold of computing time that denotes convenience for the user and has the same performance accuracy with the computer for the high-quality images. These findings motivated the development of disease detection processing on android using a smartphone camera, which aims to achieve real-time detection and classification with high accuracy.

Recent Advances in Real-Time Raman Spectroscopy for In Vivo Skin Cancer Diagnosis

2015 ◽  
Author(s):  
Jianhua Zhao ◽  
Haishan Zeng ◽  
David I. McLean ◽  
Sunil Kalia ◽  
Harvey Lui
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Real Time ◽  
Cancer Diagnosis ◽  
Recent Advances

scholarly journals Real-time Raman Spectroscopy for In Vivo Skin Cancer Diagnosis

2012 ◽  
Vol 72 (10) ◽  
pp. 2491-2500 ◽  
Author(s):  
Harvey Lui ◽  
Jianhua Zhao ◽  
David McLean ◽  
Haishan Zeng
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Real Time ◽  
Cancer Diagnosis

A MEMS based handheld confocal microscope with Raman spectroscopy for in-vivo skin cancer diagnosis

2009 ◽  
Author(s):  
Christopher L. Arrasmith ◽  
Chetan A. Patil ◽  
David L. Dickensheets ◽  
Anita Mahadevan-Jansen
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Cancer Diagnosis ◽  
Confocal Microscope

Discriminating Neoplastic and Normal Brain Tissues in Vitro Through Raman Spectroscopy: A Principal Components Analysis Classification Model

2013 ◽  
Vol 31 (12) ◽  
pp. 595-604 ◽  
Author(s):  
Ricardo Pinto Aguiar ◽  
Landulfo Silveira ◽  
Edgar Teixeira Falcão ◽  
Marcos Tadeu Tavares Pacheco ◽  
Renato Amaro Zângaro ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Classification Model ◽  
Normal Brain ◽  
Components Analysis ◽  
Brain Tissues

Formulation, Characterization and In vitro Cytotoxicity of 5-Fluorouracil Loaded Polymeric Electrospun Nanofibers for the Treatment of Skin Cancer

2019 ◽  
Vol 13 (2) ◽  
pp. 114-128 ◽  
Author(s):  
Gayatri Patel ◽  
Bindu K.N. Yadav
Keyword(s):  
Skin Cancer ◽  
Basal Cell Carcinoma ◽  
Cell Viability ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Fiber Diameter ◽  
Electrospun Nanofibers ◽  
In Vitro Cytotoxicity ◽  
Fractional Factorial

Background: The purpose of this study was to formulate, characterize and conduct in vitro cytotoxicity of 5-fluorouracil loaded polymeric electrospun nanofibers for the treatment of skin cancer. The patents on electrospun nanofibers (US9393216B2), (US14146252), (WO2015003155A1) etc. helped in the selection of polymers and method for the preparation of nanofibers. Methods: In the present study, the fabrication of nanofibers was done using a blend of chitosan with polyvinyl alcohol and processed using the electrospinning technique. 5-fluorouracil with known chemotherapeutic potential in the treatment of skin cancer was used as a drug carrier. 24-1 fractional factorial screening design was employed to study the effect of independent variables like the concentration of the polymeric solution, applied voltage (kV), distance (cm), flow rate (ml / hr) on dependent variables like % entrapment efficiency and fiber diameter. Results: Scanning electron microscopy was used to characterize fiber diameter and morphology. Results showed that the fiber diameter of all batches was found in the range of 100-200 nm. The optimized batch results showed the fiber diameter of 162.7 nm with uniform fibers. The tensile strength obtained was 190±37 Mpa. Further in vitro and ex vivo drug release profile suggested a controlled release mechanism for an extended period of 24 hr. The 5-fluorouracil loaded electrospun nanofibers were found to decrease cell viability up to ≥50% over 24 hr, with the number of cells dropping by ~ 10% over 48 hr. As the cell viability was affected by the release of 5-fluorouracil, we believe that electrospun nanofibers are a promising drug delivery system for the treatment of Basal Cell Carcinoma (BCC) skin cancer. Conclusion: These results demonstrate the possibility of delivering 5-Fluorouracil loaded electrospun nanofiber to skin with enhanced encapsulation efficiency indicating the effectiveness of the formulation for the treatment of basal cell carcinoma type of skin cancer.

scholarly journals CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chuandong Song ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Fuzzy Neural ◽  
Human Complex ◽  
Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

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