Hair and Scalp Disease Detection using Machine Learning and Image Processing

Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers

Prevalence and major risk factors of non-communicable diseases: a machine learning based cross-sectional study

The aim: The study aimed to determine the prevalence of several non-communicable diseases (NCD) and analyze risk factors among adult patients seeking nutritional guidance in Dhaka, Bangladesh. Participants: 146 hospitalized adults of both genders aged 18-93 participated in this cross-sectional research. Methods: We collected the demographic and vital information from 146 hospitalized patients in Dhaka, Bangladesh. We checked the physical and vital parameters, including blood sugar, serum creatinine, blood pressure, and the presence or absence of major non-communicable diseases. Then we used descriptive statistical approaches to explore the NCDs prevalence based on gender and age group. Afterwards, the relationship between different NCD pairs with their combined effects was analyzed using different hypothesis testing at a 95 % confidence level. Finally, the random forest and XGBoost machine learning algorithms are used to predict the comorbidity among the patients with the underlying responsible factors. Result: Our study observed the relationships between gender, age groups, obesity, and NCDs (DM, CKD, IBS, CVD, CRD, thyroid). The most frequently reported NCD was cardiovascular issues (CVD), which was present in 83.56 % of all participants. CVD was more common in male participants. Consequently, male participants had a higher blood pressure distribution than females. Diabetes mellitus (DM), on the other hand, did not have a gender-based inclination. Both CVD and DM had an age-based progression. Our study showed that chronic respiratory illness was more frequent in middle-aged participants than in younger or elderly individuals. Based on the data, every one in five hospitalized patients was obese. We analyzed the comorbidities and found that 31.5 % of the population has only one NCD, 30.1 % has two NCDs, and 38.3 % has more than two NCDs. Besides, 86.25 % of all diabetic patients had cardiovascular issues. All thyroid patients in our study had CVD. Using a t-test, we found a relationship between CKD and thyroid (p-value 0.061). Males under 35 years have a statistically significant relationship between thyroid and chronic respiratory diseases (p-value 0.018). We also found an association between DM and CKD among patients over 65 (p-value 0.038). Moreover, there has been a statistically significant relationship between CKD and Thyroid (P<0.05) for those below 35 and 35-65. We used a two-way ANOVA test to find the statistically significant interaction of heart issues and chronic respiratory illness in combination with diabetes. The combination of DM and RTI also affected CKD in male patients over 65 years old. Among machine learning algorithms, XGBoost produced the highest accuracy, 69.7 %, in comorbidity detection. Random forest feature importance detected age, weight and waist-hip ratio as the major risk factors behind the comorbidity. Conclusion: The prevalence study helps to identify the future risks and most vulnerable groups. By initiating and implementing control plans based on the prevalence study, it is possible to reduce the burden of NCDs on the elderly and middle-aged population of Bangladesh

In vitro regeneration of two high-yielding eggplant (Solanum melongena L.) varieties of Bangladesh

An in vitro regeneration protocol was developed for two high-yielding eggplant varieties (Solanum melongena L.) namely BARI begun-4 and BARI begun-6. Multiple shoots were regenerated from cotyledonary explants through organogenesis with growth regulators of different combinations and concentrations.  The best response towards multiple shoot regeneration was achieved from cotyledon explants on MS media complemented with 1 mg/l BAP + 0.2 mg/l IAA in both the two varieties of eggplant. Elongation of shoots was achieved on hormone free MS medium. Regenerated shoots of both the varieties produced   active in vitro root system on half strength of MS medium supplemented with 0.2 mg/l IBA.  The in vitro grown plantlets were acclimatized in soil, grew up to maturity, flowered, fruited and produced seeds as normal healthy plant like the control

Prevalence and major risk factors of non-communicable diseases: A Hospital-based Cross-Sectional Study in Dhaka, Bangladesh

Objective: The study aimed to determine the prevalence of several non-communicable diseases (NCD) and analyze risk factors among adult patients seeking nutritional guidance in Dhaka, Bangladesh. Result: Our study observed the relationships between gender, age groups, obesity, and NCDs (DM, CKD, IBS, CVD, CRD, thyroid). The most frequently reported NCD was cardiovascular issues (CVD), which was present in 83.56% of all participants. CVD was more common in male participants. Consequently, male participants had a higher blood pressure distribution than females. Diabetes mellitus (DM), on the other hand, did not have a gender-based inclination. Both CVD and DM had an age-based progression. Our study showed that chronic respiratory illness was more frequent in middle-aged participants than in younger or elderly individuals. Based on the data, every one in five hospitalized patients was obese. We analyzed the co-morbidities and found that 31.5% of the population has only one NCD, 30.1% has two NCDs, and 38.3% has more than two NCDs. Besides, 86.25% of all diabetic patients had cardiovascular issues. All thyroid patients in our study had CVD. Using a t-test, we found a relationship between CKD and thyroid (p-value 0.061). Males under 35 years have a statistically significant relationship between thyroid and chronic respiratory diseases (p-value 0.018). We also found an association between DM and CKD among patients over 65 (p-value 0.038). Moreover, there has been a statistically significant relationship between CKD and Thyroid (P < 0.05) for those below 35 and 35-65. We used a two-way ANOVA test to find the statistically significant interaction of heart issues and chronic respiratory illness, in combination with diabetes. The combination of DM and RTI also affected CKD in male patients over 65 years old.Comment: 25 pages, 10 figures, 3 table