Recognizing Common Skin Diseases in the Philippines Using Image Processing and Machine Learning Classification

2020 ◽  
Author(s):  
JOEL CASUAYAN DE GOMA ◽  
MADHAVI DEVARAJ
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Skin Diseases ◽  
The Philippines ◽  
Machine Learning Classification ◽  
Common Skin

scholarly journals A Review of Skin Disease Classification Techniques based on Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1097-1101
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Skin Cancer ◽  
Basal Cell Carcinoma ◽  
Actinic Keratosis ◽  
Skin Disease ◽  
Skin Diseases ◽  
Skin Condition ◽  
Disease Classification ◽  
Vascular Lesions

Dermatology is one of the most unpredictable and difficult field to diagnose. In this field, more tests are needed to be carried out so as to decide the skin condition the patient may be facing. The time to diagnose may vary according to the different dermatologist. Machine learning and image processing can be used to efficiently detect the skin diseases. There are seven different categories of skin cancer- melanocytic nevi, melanoma, benign keratosis, Basal cell carcinoma, actinic keratosis, vascular lesions and dermatofibroma. The purpose of this review is to outline types, diagnosis, methodology and treatment of skin cancer.

scholarly journals Accident Detection and Number Plate Recognition using Image Processing and Machine Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 5079-5083
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Image Enhancements ◽  
Canny Edge ◽  
Continuous Frames ◽  
Processing Techniques ◽  
Accident Detection

The purpose of this project is to detect the accident before it happens along with theextraction the number plate. Different image processing techniques along with morphological operators and Canny Edge Detection are used for image enhancements and object outline detections. With analysis of continuous frames, the relative velocity and the distance from which the leading vehicles are moving could be computed which is further helpful in accident detection and thus prevention too. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Different machine learning classification algorithms like SVM, MLP, and XGBoost are used for classification of the object. Different standard OCR tools like Pytesseract, PyOCR, TesserOCR are used for the retrieval of the vehicle number from the extracted licence plate sub-image.

scholarly journals Dermatological Disorder Detection Using Machine Learning

2020 ◽  
Vol 5 (6) ◽  
pp. 1380-1383
Author(s):  
Ashlesha Gaikwad ◽  
Meghna Sonayallu ◽  
Shivani Tilekar ◽  
A.S. Deokar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Early Diagnosis ◽  
21St Century ◽  
Skin Diseases ◽  
Bayes Classifier ◽  
Critical Part ◽  
Melanoma Prognosis ◽  
Human Interpretation

Skin diseases are considered one of the biggest scientific troubles in 21st century because of its especially complex and luxurious prognosis with problems and subjectivity of human interpretation. In cases of deadly illnesses like Melanoma prognosis in early tiers play a critical part in determining the possibility of getting cured. The software of automated strategies will assist in early diagnosis specifically with photographs with variety of analysis. Hence, in this system we present a completely automated machine of skin sickness recognition via lesion images, a device intervention in evaluation to traditional clinical personnel based detection. This system is designed into 3 levels compromising of statistics series and augmentation, designing version and subsequently prediction of disease. This proposed system uses more than one AI algorithms like Convolutional Neural Network and naive Bayes classifier and amalgamated it with image processing tools to shape a higher shape, leading to better accuracy.

Efficacy and Safety of Oral Ivermectin and Topical Permethrin in the Treatment of Scabies

2020 ◽  
Vol 33 (1) ◽  
pp. 41-47
Author(s):  
Mohsena Akhter ◽  
Ishrat Bhuiyan ◽  
Zulfiqer Hossain Khan ◽  
Mahfuza Akhter ◽  
Gulam Kazem Ali Ahmad ◽  
...  
Keyword(s):  
Skin Diseases ◽  
Close Contact ◽  
Randomized Study ◽  
Sarcoptes Scabiei ◽  
Efficacy And Safety ◽  
Common Skin ◽  
Group A ◽  
The Mean ◽  
The Difference ◽  
Group B

Background: Scabies is one of the most common skin diseases in our country. It is caused by the mite Sarcoptes scabiei var hominis, which is an ecto-parasite infesting the epidermis. Scabies is highly contagious. Prevalence is high in congested or densely populated areas. Individuals with close contact with an affected person should be treated with scabicidal which is available in both oral and topical formulations. The only oral but highly effective scabicidal known to date is Ivermectin. Amongst topical preparations, Permethrin 5 % cream is the treatment of choice. Objective: To evaluate the efficacy & safety of oral Ivermectin compared to topical Permethrin in the treatment of scabies. Methodology: This prospective, non-randomized study was conducted at the out-patient department of Dermatology and Venereology of Shaheed Suhrawardy Medical College & Hospital over a period of 6 months, from August 2016 to January 2017. The study population consisted of one hundred patients having scabies, enrolled according to inclusion criteria. They were divided into two groups. group A was subjected to oral Ivermectin and the group B to Permethrin 5% cream. Patients were followed up on day 7 and 14 for assessment of efficacy and safety. Result: The mean scoring with SD in group A (Ivermectin) and group B (Permethrin) were 8.26 ± 2.22 and 7.59 ± 2.01 respectively at the time of observation. The difference between the mean score of the two group is not significant (p=0.117) the mean scoring with SD in group A and group B were 4.54 ± 2.05 and 1.64 ± 1.84 respectively at 7thdays. The difference between the mean score of the two group is significant (p<0.001). The mean scoring with SD in group A and group B were 2.68± 2.35 and .36± 1.10 respectively at 14th day difference between the mean score of the group is significant (p<0.001). Conclusion: Topical application of permethrin 5% cream is more effective and safer than oral Ivermectin in the treatment of scabies. TAJ 2020; 33(1): 41-47

Prevalence and factors associated with anxiety among patients with common skin diseases

2020 ◽  
pp. 153-159 ◽  
Author(s):  
Homaid Al-Otaibi ◽  
Nawaf Alotibi ◽  
Fahad Althiyabi ◽  
Sami Alosaimi ◽  
Yazid Alharbi ◽  
...  
Keyword(s):  
Skin Diseases ◽  
Factors Associated ◽  
Common Skin

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

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