An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.</p

CASTLEMAN DISEASE: A GREAT MIMICKER OF METASTASES IN RADIOIODINE REFRACTORY THYROID CANCER

A 27-year-old male underwent total thyroidectomy for thyroid swelling. Histopathology showed papillary thyroid carcinoma [T3 - 6.0 cm] with extra-thyroidal extension. The patient was treated with 150 mCi radioactive iodine(RAI) as adjuvant ablative therapy. Radioiodine refractory disease was identified 1-year post-RAI therapy with elevated thyroglobulin levels and negative I-131 whole body scan. F-18 FDG positron emission tomography/computedtomography scan showed activity in the right thyroid bed and multilevel right cervical nodes. Right-sided modified neck dissection was done, which showed Castleman disease (hyaline vascular type) in right cervical nodes. The most probable cause of elevated tumour markers was found out to be 0.6 cm right thyroid bed nodule on follow-up ultrasonography. Our patient also had coexistent conditions as; osteopoikilosis and Hepatitis C along with thyroid carcinoma.Key words: Castleman disease, lymph node, radioiodine, thyroid cance

Mobile-based Skin Lesions Classification Using Convolution Neural Network

This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions

مرض الموت میں طلاق کے احکام و مسائل،ایک فقہی جائزہ: Rulings and problems of divorce in death, a jurisprudential review

A person has the right to dispose of his wealth. And he can do this disposal whether he is in a state of health or in a state of illness, but according to their nature, the order of dispositions made in a state of health and in a state of illness is not the same, because the patient's The willpower is weak and as a result of the dispositions made in the state of illness, the heirs and creditors may be harmed. It should be borne in mind that if a person is afflicted with a disease which usually causes death; But this disease does not pose much threat to his health, nor is the disease progressing; Rather, he lives for many years in spite of this disease, so in this case such a man's death will begin when his health deteriorates greatly and he dies in that condition. There are different opinions among the jurists about whether it happens or not, or what its effects are. Keywords: Rulings, problems, divorce, death, jurisprudentia

Achievements of neural network in skin lesions classification

The gross mismatch of skin disease cases and the specialties to manage them is the main cause of a continuously increased disease burden. The skin disease burden contributes 1.79% toward the global disease burden. To lessen this burden, automated skin lesions classification schemes that can provide multiclass classification are highly demanded. This chapter presents an investigation into an automated classification scheme to classify multiple skin lesions (acne, eczema, psoriasis; benign, and malignant) using state-of-the-art machine learning techniques. In the proposed classification scheme, convolution neural network (CNN) is utilized using the transfer learning approach, and a pretrained CNN model “AlexNet” is used to retrain the classification model on the skin lesion dataset. The proposed classification scheme outperformed over existing classification schemes and obtained an accuracy of 96.65%. The multiclass classification scheme can be very beneficial in the limited resource areas as it can assist in the early diagnosis of multiple skin lesions